Dynamically Mitigating Data Discrepancy with Balanced Focal Loss for Replay Attack Detection

Yongqiang Dou, Haocheng Yang, Maolin Yang, Yanyan Xu, Dengfeng Ke
Track 2: Biometrics, Human Analysis and Behavior Understanding
Thu 14 Jan 2021 at 12:00 in session PS T2.4

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Auto-TLDR; Anti-Spoofing with Balanced Focal Loss Function and Combination Features

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It becomes urgent to design effective anti-spoofing algorithms for vulnerable automatic speaker verification systems due to the advancement of high-quality playback devices. Current studies mainly treat anti-spoofing as a binary classification problem between bonafide and spoofed utterances, while lack of indistinguishable samples makes it difficult to train a robust spoofing detector. In this paper, we argue that for anti-spoofing, it needs more attention for indistinguishable samples over easily-classified ones in the modeling process, to make correct discrimination a top priority. Therefore, to mitigate the data discrepancy between training and inference, we propose to leverage a balanced focal loss function as the training objective to dynamically scale the loss based on the traits of the sample itself. Besides, in the experiments, we select three kinds of features that contain both magnitude-based and phase-based information to form complementary and informative features. Experimental results on the ASVspoof2019 dataset demonstrate the superiority of the proposed methods by comparison between our systems and top-performing ones. Systems trained with the balanced focal loss perform significantly better than conventional cross-entropy loss. With complementary features, our fusion system with only three kinds of features outperforms other systems containing five or more complex single models by 22.5% for min-tDCF and 7% for EER, achieving a min-tDCF and an EER of 0.0124 and 0.55% respectively. Furthermore, we present and discuss the evaluation results on real replay data apart from the simulated ASVspoof2019 data, indicating that research for anti-spoofing still has a long way to go.

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ResMax: Detecting Voice Spoofing Attacks with Residual Network and Max Feature Map

Il-Youp Kwak, Sungsu Kwag, Junhee Lee, Jun Ho Huh, Choong-Hoon Lee, Youngbae Jeon, Jeonghwan Hwang, Ji Won Yoon
Track 2: Biometrics, Human Analysis and Behavior Understanding
Thu 14 Jan 2021 at 12:00 in session PS T2.4

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Auto-TLDR; ASVspoof 2019: A Lightweight Automatic Speaker Verification Spoofing and Countermeasures System

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The ``2019 Automatic Speaker Verification Spoofing And Countermeasures Challenge'' (ASVspoof) competition aimed to facilitate the design of highly accurate voice spoofing attack detection systems. the competition did not emphasize model complexity and latency requirements; such constraints are strict and integral in real-world deployment. Hence, most of the top performing solutions from the competition all used an ensemble approach, and combined multiple complex deep learning models to maximize detection accuracy -- this kind of approach would sit uneasily with real-world deployment constraints. To design a lightweight system, we combined the notions of skip connection (from ResNet) and max feature map (from Light CNN), and evaluated the accuracy of the system using the ASVspoof 2019 dataset. With an optimized constant Q transform (CQT) feature, our single model achieved a replay attack detection equal error rate (EER) of 0.37% on the evaluation set, outperforming the top ensemble system from the competition that achieved an EER of 0.39%.

Face Anti-Spoofing Using Spatial Pyramid Pooling

Lei Shi, Zhuo Zhou, Zhenhua Guo
Track 2: Biometrics, Human Analysis and Behavior Understanding
Wed 13 Jan 2021 at 12:00 in session PS T2.2

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Auto-TLDR; Spatial Pyramid Pooling for Face Anti-Spoofing

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Face recognition system is vulnerable to many kinds of presentation attacks, so how to effectively detect whether the image is from the real face is particularly important. At present, many deep learning-based anti-spoofing methods have been proposed. But these approaches have some limitations, for example, global average pooling (GAP) easily loses local information of faces, single-scale features easily ignore information differences in different scales, while a complex network is prune to be overfitting. In this paper, we propose a face anti-spoofing approach using spatial pyramid pooling (SPP). Firstly, we use ResNet-18 with a small amount of parameter as the basic model to avoid overfitting. Further, we use spatial pyramid pooling module in the single model to enhance local features while fusing multi-scale information. The effectiveness of the proposed method is evaluated on three databases, CASIA-FASD, Replay-Attack and CASIA-SURF. The experimental results show that the proposed approach can achieve state-of-the-art performance.

Face Anti-Spoofing Based on Dynamic Color Texture Analysis Using Local Directional Number Pattern

Junwei Zhou, Ke Shu, Peng Liu, Jianwen Xiang, Shengwu Xiong
Track 2: Biometrics, Human Analysis and Behavior Understanding
Thu 14 Jan 2021 at 12:00 in session PS T2.4

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Auto-TLDR; LDN-TOP Representation followed by ProCRC Classification for Face Anti-Spoofing

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Face anti-spoofing is becoming increasingly indispensable for face recognition systems, which are vulnerable to various spoofing attacks performed using fake photos and videos. In this paper, a novel "LDN-TOP representation followed by ProCRC classification" pipeline for face anti-spoofing is proposed. We use local directional number pattern (LDN) with the derivative-Gaussian mask to capture detailed appearance information resisting illumination variations and noises, which can influence the texture pattern distribution. To further capture motion information, we extend LDN to a spatial-temporal variant named local directional number pattern from three orthogonal planes (LDN-TOP). The multi-scale LDN-TOP capturing complete information is extracted from color images to generate the feature vector with powerful representation capacity. Finally, the feature vector is fed into the probabilistic collaborative representation based classifier (ProCRC) for face anti-spoofing. Our method is evaluated on three challenging public datasets, namely CASIA FASD, Replay-Attack database, and UVAD database using sequence-based evaluation protocol. The experimental results show that our method can achieve promising performance with 0.37% EER on CASIA and 5.73% HTER on UVAD. The performance on Replay-Attack database is also competitive.

A Cross Domain Multi-Modal Dataset for Robust Face Anti-Spoofing

Qiaobin Ji, Shugong Xu, Xudong Chen, Shan Cao, Shunqing Zhang
Track 2: Biometrics, Human Analysis and Behavior Understanding
Thu 14 Jan 2021 at 12:00 in session PS T2.4

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Auto-TLDR; Cross domain multi-modal FAS dataset GREAT-FASD and several evaluation protocols for academic community

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Face Anti-spoofing (FAS) is a challenging problem due to the complex serving scenario and diverse face presentation attack patterns. Using single modal images which are usually captured with RGB cameras is not able to deal with the former because of serious overfitting problems. The existing multi-modal FAS datasets rarely pay attention to the cross domain problems, trainingFASsystemonthesedataleadstoinconsistenciesandlow generalization capabilities in deployment since imaging principles(structured light, TOF, etc.) and pre-processing methods vary between devices. We explore the subtle fine-grained differences betweeen multi-modal cameras and proposed a cross domain multi-modal FAS dataset GREAT-FASD and several evaluation protocols for academic community. Furthermore, we incorporate the multiplicative attention and center loss to enhance the representative power of CNN via seeking out complementary information as a powerful baseline. In addition, extensive experiments have been conducted on the proposed dataset to analyze the robustness to distinguish spoof faces and bona-fide faces. Experimental results show the effectiveness of proposed method and achieve the state-of-the-art competitive results. Finally, we visualize our future distribution in hidden space and observe that the proposed method is able to lead the network to generate a large margin for face anti-spoofing task

MixNet for Generalized Face Presentation Attack Detection

Nilay Sanghvi, Sushant Singh, Akshay Agarwal, Mayank Vatsa, Richa Singh
Track 2: Biometrics, Human Analysis and Behavior Understanding
Thu 14 Jan 2021 at 12:00 in session PS T2.4

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Auto-TLDR; MixNet: A Deep Learning-based Network for Detection of Presentation Attacks in Cross-Database and Unseen Setting

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The non-intrusive nature and high accuracy of face recognition algorithms have led to their successful deployment across multiple applications ranging from border access to mobile unlocking and digital payments. However, their vulnerability against sophisticated and cost-effective presentation attack mediums raises essential questions regarding its reliability. Several presentation attack detection algorithms are presented; however, they are still far behind from reality. The major problem with the existing work is the generalizability against multiple attacks both in the seen and unseen setting. The algorithms which are useful for one kind of attack (such as print) fail miserably for another type of attack (such as silicone masks). In this research, we have proposed a deep learning-based network called MixNet to detect presentation attacks in cross-database and unseen attack settings. The proposed algorithm utilizes state-of-the-art convolutional neural network architectures and learns the feature mapping for each attack category. Experiments are performed using multiple challenging face presentation attack databases such as Silicone Mask Attack Database (SMAD) and Spoof In the Wild with Multiple Attack (SiW-M). Extensive experiments and comparison with the existing state of the art algorithms show the effectiveness of the proposed algorithm.

ESResNet: Environmental Sound Classification Based on Visual Domain Models

Andrey Guzhov, Federico Raue, Jörn Hees, Andreas Dengel
Track 5: Image and Signal Processing
Thu 14 Jan 2021 at 16:00 in session PS T5.6

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Auto-TLDR; Environmental Sound Classification with Short-Time Fourier Transform Spectrograms

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Environmental Sound Classification (ESC) is an active research area in the audio domain and has seen a lot of progress in the past years. However, many of the existing approaches achieve high accuracy by relying on domain-specific features and architectures, making it harder to benefit from advances in other fields (e.g., the image domain). Additionally, some of the past successes have been attributed to a discrepancy of how results are evaluated (i.e., on unofficial splits of the UrbanSound8K (US8K) dataset), distorting the overall progression of the field. The contribution of this paper is twofold. First, we present a model that is inherently compatible with mono and stereo sound inputs. Our model is based on simple log-power Short-Time Fourier Transform (STFT) spectrograms and combines them with several well-known approaches from the image domain (i.e., ResNet, Siamese-like networks and attention). We investigate the influence of cross-domain pre-training, architectural changes, and evaluate our model on standard datasets. We find that our model out-performs all previously known approaches in a fair comparison by achieving accuracies of 97.0 % (ESC-10), 91.5 % (ESC-50) and 84.2 % / 85.4 % (US8K mono / stereo). Second, we provide a comprehensive overview of the actual state of the field, by differentiating several previously reported results on the US8K dataset between official or unofficial splits. For better reproducibility, our code (including any re-implementations) is made available.

Hybrid Network for End-To-End Text-Independent Speaker Identification

Wajdi Ghezaiel, Luc Brun, Olivier Lezoray
Track 2: Biometrics, Human Analysis and Behavior Understanding
Wed 13 Jan 2021 at 14:00 in session PS T2.3

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Auto-TLDR; Text-Independent Speaker Identification with Scattering Wavelet Network and Convolutional Neural Networks

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Deep learning has recently improved the performance of Speaker Identification (SI) systems. Promising results have been obtained with Convolutional Neural Networks (CNNs). This success are mostly driven by the advent of large datasets. However in the context of commercial applications, collection of large amount of training data is not always possible. In addition, robustness of a SI system is adversely effected by short utterances. SI with only a few and short utterances is a challenging problem. Therefore, in this paper, we propose a novel text-independent speaker identification system. The proposed system can identify speakers by learning from only few training short utterances examples. To achieve this, we combine CNN with Scattering Wavelet Network. We propose a two-stage feature extraction framework using a two-layer wavelet scattering network coupled with a CNN for SI system. The proposed architecture takes variable length speech segments. To evaluate the effectiveness of the proposed approach, Timit and Librispeech datasets are used in the experiments. These conducted experiments show that our hybrid architecture performs successfully for SI, even with a small number and short duration of training samples. In comparaison with related methods, the obtained results shows that an hybrid architecture achieve better performance.

Detection of Calls from Smart Speaker Devices

Vinay Maddali, David Looney, Kailash Patil
Track 5: Image and Signal Processing
Tue 12 Jan 2021 at 17:00 in session PS T5.1

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Auto-TLDR; Distinguishing Between Smart Speaker and Cell Devices Using Only the Audio Using a Feature Set

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The ubiquity of smart speakers is increasing, with a growing number of households utilising these devices to make calls over the telephony network. As the technology is typically configured to retain the cellular phone number of the user, it presents challenges in applications where knowledge of the true call origin is required. There are a wide range of makes and models for these devices, as is the case with cell phones, and it is challenging to detect the general category as a smart speaker or cell, independent of the designated phone number. In this paper, we present an approach to differentiate between calls originating from smart speakers and ones from cellular devices using only the audio. We present a feature set that characterises the relevant acoustic information, such as the degree of reverberation and noise, to distinguish between these categories. When evaluated on a dataset spanning multiple models for each device category, as well as different modes-of-usage and microphone-speaker distances, the method yields an Equal Error Rate (EER) of 12.6%.

Disentangled Representation Based Face Anti-Spoofing

Zhao Liu, Zunlei Feng, Yong Li, Zeyu Zou, Rong Zhang, Mingli Song, Jianping Shen
Track 2: Biometrics, Human Analysis and Behavior Understanding
Wed 13 Jan 2021 at 12:00 in session PS T2.2

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Auto-TLDR; Face Anti-Spoofing using Motion Information and Disentangled Frame Work

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Face anti-spoofing is an important problem for both academic research and industrial face recognition systems. Most of the existing face anti-spoofing methods take it as a classification task on individual static images, where motion pattern differences in consecutive real or fake face sequences are ignored. In this work, we propose a novel method to identify spoofing patterns using motion information. Different from previous methods, the proposed method makes the real or fake decision on the disentangled feature level, based on the observation that motion and spoofing pattern features could be disentangled from original image frames. We design a representation disentangling frame- work for this task, which is able to reconstruct both real and fake face sequences from the input. Meanwhile, the disentangled representations could be used to classify whether the input faces are real or fake. We perform several experiments on Casia-FASD and ReplayAttack datasets. The proposed method achieves SOTA results compared with existing face anti-spoofing methods.

Feature Engineering and Stacked Echo State Networks for Musical Onset Detection

Peter Steiner, Azarakhsh Jalalvand, Simon Stone, Peter Birkholz
Track 1: Artificial Intelligence, Machine Learning for Pattern Analysis
Wed 13 Jan 2021 at 16:30 in session PS T1.8

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Auto-TLDR; Echo State Networks for Onset Detection in Music Analysis

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In music analysis, one of the most fundamental tasks is note onset detection - detecting the beginning of new note events. As the target function of onset detection is related to other tasks, such as beat tracking or tempo estimation, onset detection is the basis for such related tasks. Furthermore, it can help to improve Automatic Music Transcription (AMT). Typically, different approaches for onset detection follow a similar outline: An audio signal is transformed into an Onset Detection Function (ODF), which should have rather low values (i.e. close to zero) for most of the time but with pronounced peaks at onset times, which can then be extracted by applying peak picking algorithms on the ODF. In the recent years, several kinds of neural networks were used successfully to compute the ODF from feature vectors. Currently, Convolutional Neural Networks (CNNs) define the state of the art. In this paper, we build up on an alternative approach to obtain a ODF by Echo State Networks (ESNs), which have achieved comparable results to CNNs in several tasks, such as speech and image recognition. In contrast to the typical iterative training procedures of deep learning architectures, such as CNNs or networks consisting of Long-Short-Term Memory Cells (LSTMs), in ESNs only a very small part of the weights is easily trained in one shot using linear regression. By comparing the performance of several feature extraction methods, pre-processing steps and introducing a new way to stack ESNs, we expand our previous approach to achieve results that fall between a bidirectional LSTM network and a CNN with relative improvements of 1.8% and -1.4%, respectively. For the evaluation, we used exactly the same 8-fold cross validation setup as for the reference results.

Are Spoofs from Latent Fingerprints a Real Threat for the Best State-Of-Art Liveness Detectors?

Roberto Casula, Giulia Orrù, Daniele Angioni, Xiaoyi Feng, Gian Luca Marcialis, Fabio Roli
Track 2: Biometrics, Human Analysis and Behavior Understanding
Fri 15 Jan 2021 at 13:00 in session OS T2.3

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Auto-TLDR; ScreenSpoof: Attacks using latent fingerprints against state-of-art fingerprint liveness detectors and verification systems

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We investigated the threat level of realistic attacks using latent fingerprints against sensors equipped with state-of-art liveness detectors and fingerprint verification systems which integrate such liveness algorithms. To the best of our knowledge, only a previous investigation was done with spoofs from latent prints. In this paper, we focus on using snapshot pictures of latent fingerprints. These pictures provide molds, that allows, after some digital processing, to fabricate high-quality spoofs. Taking a snapshot picture is much simpler than developing fingerprints left on a surface by magnetic powders and lifting the trace by a tape. What we are interested here is to evaluate preliminary at which extent attacks of the kind can be considered a real threat for state-of-art fingerprint liveness detectors and verification systems. To this aim, we collected a novel data set of live and spoof images fabricated with snapshot pictures of latent fingerprints. This data set provide a set of attacks at the most favourable conditions. We refer to this method and the related data set as "ScreenSpoof". Then, we tested with it the performances of the best liveness detection algorithms, namely, the three winners of the LivDet competition. Reported results point out that the ScreenSpoof method is a threat of the same level, in terms of detection and verification errors, than that of attacks using spoofs fabricated with the full consensus of the victim. We think that this is a notable result, never reported in previous work.

Toward Text-Independent Cross-Lingual Speaker Recognition Using English-Mandarin-Taiwanese Dataset

Yi-Chieh Wu, Wen-Hung Liao
Track 2: Biometrics, Human Analysis and Behavior Understanding
Fri 15 Jan 2021 at 15:00 in session PS T2.5

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Auto-TLDR; Cross-lingual Speech for Biometric Recognition

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Over 40% of the world's population is bilingual. Existing speaker identification/verification systems, however, assume the same language type for both enrollment and recognition stages. In this work, we investigate the feasibility of employing multilingual speech for biometric application. We establish a dataset containing audio recorded in English, Mandarin and Taiwanese. Three acoustic features, namely, i-vector, d-vector and x-vector have been evaluated for both speaker verification (SV) and identification (SI) tasks. Preliminary experimental results indicate that x-vector achieves the best overall performance. Additionally, model trained with hybrid data demonstrates highest accuracy associated with the cost of data collection efforts. In SI tasks, we obtained over 91\% cross-lingual accuracy all models using 3-second audio. In SV tasks, the EER among cross-lingual test is at most 6.52\%, which is observed on the model trained by English corpus. The outcome suggests the feasibility of adopting cross-lingual speech in building text-independent speaker recognition systems.

Improving Gravitational Wave Detection with 2D Convolutional Neural Networks

Siyu Fan, Yisen Wang, Yuan Luo, Alexander Michael Schmitt, Shenghua Yu
Track 5: Image and Signal Processing
Tue 12 Jan 2021 at 17:00 in session PS T5.2

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Auto-TLDR; Two-dimensional Convolutional Neural Networks for Gravitational Wave Detection from Time Series with Background Noise

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Sensitive gravitational wave (GW) detectors such as that of Laser Interferometer Gravitational-wave Observatory (LIGO) realize the direct observation of GW signals that confirm Einstein's general theory of relativity. However, it remains challenges to quickly detect faint GW signals from a large number of time series with background noise under unknown probability distributions. Traditional methods such as matched-filtering in general assume Additive White Gaussian Noise (AWGN) and are far from being real-time due to its high computational complexity. To avoid these weaknesses, one-dimensional (1D) Convolutional Neural Networks (CNNs) are introduced to achieve fast online detection in milliseconds but do not have enough consideration on the trade-off between the frequency and time features, which will be revisited in this paper through data pre-processing and subsequent two-dimensional (2D) CNNs during offline training to improve the online detection sensitivity. In this work, the input data is pre-processed to form a 2D spectrum by Short-time Fourier transform (STFT), where frequency features are extracted without learning. Then, carrying out two 1D convolutions across time and frequency axes respectively, and concatenating the time-amplitude and frequency-amplitude feature maps with equal proportion subsequently, the frequency and time features are treated equally as the input of our following two-dimensional CNNs. The simulation of our above ideas works on a generated data set with uniformly varying SNR (2-17), which combines the GW signal generated by PYCBC and the background noise sampled directly from LIGO. Satisfying the real-time online detection requirement without noise distribution assumption, the experiments of this paper demonstrate better performance in average compared to that of 1D CNNs, especially in the cases of lower SNR (4-9).

Generalized Iris Presentation Attack Detection Algorithm under Cross-Database Settings

Mehak Gupta, Vishal Singh, Akshay Agarwal, Mayank Vatsa, Richa Singh
Track 2: Biometrics, Human Analysis and Behavior Understanding
Thu 14 Jan 2021 at 12:00 in session PS T2.4

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Auto-TLDR; MVNet: A Deep Learning-based PAD Network for Iris Recognition against Presentation Attacks

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The deployment of biometrics features based person identification has increased significantly from border access to mobile unlock to electronic transactions. Iris recognition is considered as one of the most accurate biometric modality for person identification. However, the vulnerability of this recognition towards presentation attacks, especially towards the 3D contact lenses, can limit its potential deployments. The textured lenses are so effective in hiding the real texture of iris that it can fool not only the automatic recognition algorithms but also the human examiners. While in literature, several presentation attack detection (PAD) algorithms are presented; however, the significant limitation is the generalizability against an unseen database, unseen sensor, and different imaging environment. Inspired by the success of the hybrid algorithm or fusion of multiple detection networks, we have proposed a deep learning-based PAD network that utilizes multiple feature representation layers. The computational complexity is an essential factor in training the deep neural networks; therefore, to limit the computational complexity while learning multiple feature representation layers, a base model is kept the same. The network is trained end-to-end using a softmax classifier. We have evaluated the performance of the proposed network termed as MVNet using multiple databases such as IIITD-WVU MUIPA, IIITD-WVU UnMIPA database under cross-database training-testing settings. The experiments are performed extensively to assess the generalizability of the proposed algorithm.

DenseRecognition of Spoken Languages

Jaybrata Chakraborty, Bappaditya Chakraborty, Ujjwal Bhattacharya
Track 5: Image and Signal Processing
Fri 15 Jan 2021 at 16:00 in session PS T5.8

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Auto-TLDR; DenseNet: A Dense Convolutional Network Architecture for Speech Recognition in Indian Languages

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In the present study, we have, for the first time, con- sidered a large number of Indian languages for recog- nition from their audio signals of different sources. A dense convolutional network architecture (DenseNet) has been proposed for this classification problem. Dy- namic elimination of low energy frames from the input speech signal has been considered as a preprocessing operation. Mel-spectrogram of pre-processed speech signal is fed to a DenseNet architecture for recogni- tion of its language. Recognition performance of the proposed architecture has been compared with that of several state-of-the-art deep architectures which include a traditional convolutional neural network (CNN), multiple ResNet architectures, CNN-BLSTM and DenseNet-BLSTM hybrid architectures. Addition- ally, we obtained recognition performances of a stacked BLSTM architecture fed with different sets of hand- crafted features for comparison purpose. Simulations have been performed on two different standard datasets which include (i) IITKGP-MLILSC dataset of news clips in 27 different Indian languages and (ii) Linguistic Data Consortium (LDC) dataset of telephonic conver- sations in 5 different Indian languages. Recognition performance of the proposed framework has been found to be consistently and significantly better than all other frameworks implemented in this study.

Multi-Label Contrastive Focal Loss for Pedestrian Attribute Recognition

Xiaoqiang Zheng, Zhenxia Yu, Lin Chen, Fan Zhu, Shilong Wang
Track 2: Biometrics, Human Analysis and Behavior Understanding
Fri 15 Jan 2021 at 15:00 in session PS T2.5

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Auto-TLDR; Multi-label Contrastive Focal Loss for Pedestrian Attribute Recognition

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Pedestrian Attribute Recognition (PAR) has received extensive attention during the past few years. With the advances of deep constitutional neural networks (CNNs), the performance of PAR has been significantly improved. Existing methods tend to acquire attribute-specific features by designing various complex network structures with additional modules. Such additional modules, however, dramatically increase the number of parameters. Meanwhile, the problems of class imbalance and hard attribute retrieving remain underestimated in PAR. In this paper, we explore the optimization mechanism of the training processing to account for these problems and propose a new loss function called Multi-label Contrastive Focal Loss (MCFL). This proposed MCFL emphasizes the hard and minority attributes by using a separated re-weighting mechanism for different positive and negative classes to alleviate the impact of the imbalance. MCFL is also able to enlarge the gaps between the intra-class of multi-label attributes, to force CNNs to extract more subtle discriminative features. We evaluate the proposed MCFL on three large public pedestrian datasets, including RAP, PA-100K, and PETA. The experimental results indicate that the proposed MCFL with the ResNet-50 backbone is able to outperform other state-of-the-art approaches in comparison.

Video Face Manipulation Detection through Ensemble of CNNs

Nicolo Bonettini, Edoardo Daniele Cannas, Sara Mandelli, Luca Bondi, Paolo Bestagini, Stefano Tubaro
Track 5: Image and Signal Processing
Tue 12 Jan 2021 at 14:00 in session OS T5.1

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Auto-TLDR; Face Manipulation Detection in Video Sequences Using Convolutional Neural Networks

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In the last few years, several techniques for facial manipulation in videos have been successfully developed and made available to the masses (i.e., FaceSwap, deepfake, etc.). These methods enable anyone to easily edit faces in video sequences with incredibly realistic results and a very little effort. Despite the usefulness of these tools in many fields, if used maliciously, they can have a significantly bad impact on society (e.g., fake news spreading, cyber bullying through fake revenge porn). The ability of objectively detecting whether a face has been manipulated in a video sequence is then a task of utmost importance. In this paper, we tackle the problem of face manipulation detection in video sequences targeting modern facial manipulation techniques. In particular, we study the ensembling of different trained Convolutional Neural Network (CNN) models. In the proposed solution, different models are obtained starting from a base network (i.e., EfficientNetB4) making use of two different concepts: (i) attention layers; (ii) siamese training. We show that combining these networks leads to promising face manipulation detection results on two publicly available datasets with more than 119000 videos.

Verifying the Causes of Adversarial Examples

Honglin Li, Yifei Fan, Frieder Ganz, Tony Yezzi, Payam Barnaghi
Track 1: Artificial Intelligence, Machine Learning for Pattern Analysis
Thu 14 Jan 2021 at 16:00 in session PS T1.12

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Auto-TLDR; Exploring the Causes of Adversarial Examples in Neural Networks

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The robustness of neural networks is challenged by adversarial examples that contain almost imperceptible perturbations to inputs which mislead a classifier to incorrect outputs in high confidence. Limited by the extreme difficulty in examining a high-dimensional image space thoroughly, research on explaining and justifying the causes of adversarial examples falls behind studies on attacks and defenses. In this paper, we present a collection of potential causes of adversarial examples and verify (or partially verify) them through carefully-designed controlled experiments. The major causes of adversarial examples include model linearity, one-sum constraint, and geometry of the categories. To control the effect of those causes, multiple techniques are applied such as $L_2$ normalization, replacement of loss functions, construction of reference datasets, and novel models using multi-layer perceptron probabilistic neural networks (MLP-PNN) and density estimation (DE). Our experiment results show that geometric factors tend to be more direct causes and statistical factors magnify the phenomenon, especially for assigning high prediction confidence. We hope this paper will inspire more studies to rigorously investigate the root causes of adversarial examples, which in turn provide useful guidance on designing more robust models.

Trainable Spectrally Initializable Matrix Transformations in Convolutional Neural Networks

Michele Alberti, Angela Botros, Schuetz Narayan, Rolf Ingold, Marcus Liwicki, Mathias Seuret
Track 1: Artificial Intelligence, Machine Learning for Pattern Analysis
Tue 12 Jan 2021 at 15:00 in session PS T1.2

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Auto-TLDR; Trainable and Spectrally Initializable Matrix Transformations for Neural Networks

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In this work, we introduce a new architectural component to Neural Networks (NN), i.e., trainable and spectrally initializable matrix transformations on feature maps. While previous literature has already demonstrated the possibility of adding static spectral transformations as feature processors, our focus is on more general trainable transforms. We study the transforms in various architectural configurations on four datasets of different nature: from medical (ColorectalHist, HAM10000) and natural (Flowers) images to historical documents (CB55). With rigorous experiments that control for the number of parameters and randomness, we show that networks utilizing the introduced matrix transformations outperform vanilla neural networks. The observed accuracy increases appreciably across all datasets. In addition, we show that the benefit of spectral initialization leads to significantly faster convergence, as opposed to randomly initialized matrix transformations. The transformations are implemented as auto-differentiable PyTorch modules that can be incorporated into any neural network architecture. The entire code base is open-source.

A Weak Coupling of Semi-Supervised Learning with Generative Adversarial Networks for Malware Classification

Shuwei Wang, Qiuyun Wang, Zhengwei Jiang, Xuren Wang, Rongqi Jing
Track 5: Image and Signal Processing
Fri 15 Jan 2021 at 15:00 in session PS T5.7

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Auto-TLDR; IMIR: An Improved Malware Image Rescaling Algorithm Using Semi-supervised Generative Adversarial Network

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Malware classification helps to understand its purpose and is also an important part of attack detection. And it is also an important part of discovering attacks. Due to continuous innovation and development of artificial intelligence, it is a trend to combine deep learning with malware classification. In this paper, we propose an improved malware image rescaling algorithm (IMIR) based on local mean algorithm. Its main goal of IMIR is to reduce the loss of information from samples during the process of converting binary files to image files. Therefore, we construct a neural network structure based on VGG model, which is suitable for image classification. In the real world, a mass of malware family labels are inaccurate or lacking. To deal with this situation, we propose a novel method to train the deep neural network by Semi-supervised Generative Adversarial Network (SGAN), which only needs a small amount of malware that have accurate labels about families. By integrating SGAN with weak coupling, we can retain the weak links of supervised part and unsupervised part of SGAN. It improves the accuracy of malware classification by making classifiers more independent of discriminators. The results of experimental demonstrate that our model achieves exhibiting favorable performance. The recalls of each family in our data set are all higher than 93.75%.

Adversarially Training for Audio Classifiers

Raymel Alfonso Sallo, Mohammad Esmaeilpour, Patrick Cardinal
Track 1: Artificial Intelligence, Machine Learning for Pattern Analysis
Wed 13 Jan 2021 at 16:30 in session PS T1.8

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Auto-TLDR; Adversarially Training for Robust Neural Networks against Adversarial Attacks

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In this paper, we investigate the potential effect of the adversarially training on the robustness of six advanced deep neural networks against a variety of targeted and non-targeted adversarial attacks. We firstly show that, the ResNet-56 model trained on the 2D representation of the discrete wavelet transform appended with the tonnetz chromagram outperforms other models in terms of recognition accuracy. Then we demonstrate the positive impact of adversarially training on this model as well as other deep architectures against six types of attack algorithms (white and black-box) with the cost of the reduced recognition accuracy and limited adversarial perturbation. We run our experiments on two benchmarking environmental sound datasets and show that without any imposed limitations on the budget allocations for the adversary, the fooling rate of the adversarially trained models can exceed 90%. In other words, adversarial attacks exist in any scales, but they might require higher adversarial perturbations compared to non-adversarially trained models.

Radar Image Reconstruction from Raw ADC Data Using Parametric Variational Autoencoder with Domain Adaptation

Michael Stephan, Thomas Stadelmayer, Avik Santra, Georg Fischer, Robert Weigel, Fabian Lurz
Track 5: Image and Signal Processing
Fri 15 Jan 2021 at 16:00 in session PS T5.8

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Auto-TLDR; Parametric Variational Autoencoder-based Human Target Detection and Localization for Frequency Modulated Continuous Wave Radar

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This paper presents a parametric variational autoencoder-based human target detection and localization framework working directly with the raw analog-to-digital converter data from the frequency modulated continuous wave radar. We propose a parametrically constrained variational autoencoder, with residual and skip connections, capable of generating the clustered and localized target detections on the range-angle image. Furthermore, to circumvent the problem of training the proposed neural network on all possible scenarios using real radar data, we propose domain adaptation strategies whereby we first train the neural network using ray tracing based model data and then adapt the network to work on real sensor data. This strategy ensures better generalization and scalability of the proposed neural network even though it is trained with limited radar data. We demonstrate the superior detection and localization performance of our proposed solution compared to the conventional signal processing pipeline and earlier state-of-art deep U-Net architecture with range-doppler images as inputs.

EasiECG: A Novel Inter-Patient Arrhythmia Classification Method Using ECG Waves

Chuanqi Han, Ruoran Huang, Fang Yu, Xi Huang, Li Cui
Track 5: Image and Signal Processing
Wed 13 Jan 2021 at 12:00 in session PS T5.3

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Auto-TLDR; EasiECG: Attention-based Convolution Factorization Machines for Arrhythmia Classification

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Abstract—In an ECG record, the PQRST waves are of important medical significance which provide ample information reflecting heartbeat activities. In this paper, we propose a novel arrhythmia classification method namely EasiECG, characterized by simplicity and accuracy. Compared with other works, the EasiECG takes the configuration of these five key waves into account and does not require complicated feature engineering. Meanwhile, an additional encoding of the extracted features makes the EasiECG applicable even on samples with missing waves. To automatically capture interactions that contribute to the classification among the processed features, a novel adapted classification model named Attention-based Convolution Factorization Machines (ACFM) is proposed. In detail, the ACFM can learn both linear and high-order interactions from linear regression and convolution on outer-product feature interaction maps, respectively. After that, an attention mechanism implemented in the model can further assign different importance of these interactions when predicting certain types of heartbeats. To validate the effectiveness and practicability of our EasiECG, extensive experiments of inter-patient paradigm on the benchmark MIT-BIH arrhythmia database are conducted. To tackle the imbalanced sample problem in this dataset, an ingenious loss function: focal loss is adopted when training. The experiment results show that our method is competitive compared with other state-of-the-arts, especially in classifying the Supraventricular ectopic beats. Besides, the EasiECG achieves an overall accuracy of 87.6% on samples with a missing wave in the related experiment, demonstrating the robustness of our proposed method.

Three-Dimensional Lip Motion Network for Text-Independent Speaker Recognition

Jianrong Wang, Tong Wu, Shanyu Wang, Mei Yu, Qiang Fang, Ju Zhang, Li Liu
Track 2: Biometrics, Human Analysis and Behavior Understanding
Wed 13 Jan 2021 at 12:00 in session PS T2.2

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Auto-TLDR; Lip Motion Network for Text-Independent and Text-Dependent Speaker Recognition

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Lip motion reflects behavior characteristics of speakers, and thus can be used as a new kind of biometrics in speaker recognition. In the literature, lots of works used two dimensional (2D) lip images to recognize speaker in a text-dependent context. However, 2D lip easily suffers from face orientations. To this end, in this work, we present a novel end-to-end 3D lip motion Network (3LMNet) by utilizing the sentence-level 3D lip motion (S3DLM) to recognize speakers in both the text-independent and text-dependent contexts. A novel regional feedback module (RFM) is proposed to explore attentions in different lip regions. Besides, prior knowledge of lip motion is investigated to complement RFM, where landmark-level and frame-level features are merged to form a better feature representation. Moreover, we present two methods, i.e., coordinate transformation and face posture correction to pre-process the LSD-AV dataset, which contains 68 speakers and 146 sentences per speaker. The evaluation results on this dataset demonstrate that our proposed 3LMNet is superior to the baseline models, i.e., LSTM, VGG-16 and ResNet-34, and outperforms the state-of-the-art using 2D lip image as well as the 3D face. The code of this work is released at https://github.com/wutong18/Three-Dimensional-Lip-Motion-Ne twork-for-Text-Independent-Speaker-Recognition.

Detection of Makeup Presentation Attacks Based on Deep Face Representations

Christian Rathgeb, Pawel Drozdowski, Christoph Busch
Track 2: Biometrics, Human Analysis and Behavior Understanding
Tue 12 Jan 2021 at 17:00 in session PS T2.1

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Auto-TLDR; An Attack Detection Scheme for Face Recognition Using Makeup Presentation Attacks

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Facial cosmetics have the ability to substantially alter the facial appearance, which can negatively affect the decisions of a face recognition. In addition, it was recently shown that the application of makeup can be abused to launch so-called makeup presentation attacks. In such attacks, the attacker might apply heavy makeup in order to achieve the facial appearance of a target subject for the purpose of impersonation. In this work, we assess the vulnerability of a COTS face recognition system to makeup presentation attacks employing the publicly available Makeup Induced Face Spoofing (MIFS) database. It is shown that makeup presentation attacks might seriously impact the security of the face recognition system. Further, we propose an attack detection scheme which distinguishes makeup presentation attacks from genuine authentication attempts by analysing differences in deep face representations obtained from potential makeup presentation attacks and corresponding target face images. The proposed detection system employs a machine learning-based classifier, which is trained with synthetically generated makeup presentation attacks utilizing a generative adversarial network for facial makeup transfer in conjunction with image warping. Experimental evaluations conducted using the MIFS database reveal a detection equal error rate of 0.7% for the task of separating genuine authentication attempts from makeup presentation attacks.

3D Facial Matching by Spiral Convolutional Metric Learning and a Biometric Fusion-Net of Demographic Properties

Soha Sadat Mahdi, Nele Nauwelaers, Philip Joris, Giorgos Bouritsas, Imperial London, Sergiy Bokhnyak, Susan Walsh, Mark Shriver, Michael Bronstein, Peter Claes
Track 2: Biometrics, Human Analysis and Behavior Understanding
Thu 14 Jan 2021 at 14:00 in session OS T2.2

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Auto-TLDR; Multi-biometric Fusion for Biometric Verification using 3D Facial Mesures

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Face recognition is a widely accepted biometric verification tool, as the face contains a lot of information about the identity of a person. In this study, a 2-step neural-based pipeline is presented for matching 3D facial shape to multiple DNA-related properties (sex, age, BMI and genomic background). The first step consists of a triplet loss-based metric learner that compresses facial shape into a lower dimensional embedding while preserving information about the property of interest. Most studies in the field of metric learning have only focused on Euclidean data. In this work, geometric deep learning is employed to learn directly from 3D facial meshes. To this end, spiral convolutions are used along with a novel mesh-sampling scheme that retains uniformly sampled 3D points at different levels of resolution. The second step is a multi-biometric fusion by a fully connected neural network. The network takes an ensemble of embeddings and property labels as input and returns genuine and imposter scores. Since embeddings are accepted as an input, there is no need to train classifiers for the different properties and available data can be used more efficiently. Results obtained by a 10-fold cross-validation for biometric verification show that combining multiple properties leads to stronger biometric systems. Furthermore, the proposed neural-based pipeline outperforms a linear baseline, which consists of principal component analysis, followed by classification with linear support vector machines and a Naïve Bayes-based score-fuser.

Audio-Visual Speech Recognition Using a Two-Step Feature Fusion Strategy

Hong Liu, Wanlu Xu, Bing Yang
Track 1: Artificial Intelligence, Machine Learning for Pattern Analysis
Thu 14 Jan 2021 at 12:00 in session PS T1.10

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Auto-TLDR; A Two-Step Feature Fusion Network for Speech Recognition

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Lip-reading methods and fusion strategy are crucial for audio-visual speech recognition. In recent years, most approaches involve two separate audio and visual streams with early or late fusion strategies. Such a single-stage fusion method may fail to guarantee the integrity and representativeness of fusion information simultaneously. This paper extends a traditional single-stage fusion network to a two-step feature fusion network by adding an audio-visual early feature fusion (AV-EFF) stream to the baseline model. This method can learn the fusion information of different stages, preserving the original features as much as possible and ensuring the independence of different features. Besides, to capture long-range dependencies of video information, a non-local block is added to the feature extraction part of the visual stream (NL-Visual) to obtain the long-term spatio-temporal features. Experimental results on the two largest public datasets in English (LRW) and Mandarin (LRW-1000) demonstrate our method is superior to other state-of-the-art methods.

Attack-Agnostic Adversarial Detection on Medical Data Using Explainable Machine Learning

Matthew Watson, Noura Al Moubayed
Track 5: Image and Signal Processing
Tue 12 Jan 2021 at 17:00 in session PS T5.2

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Auto-TLDR; Explainability-based Detection of Adversarial Samples on EHR and Chest X-Ray Data

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Explainable machine learning has become increasingly prevalent, especially in healthcare where explainable models are vital for ethical and trusted automated decision making. Work on the susceptibility of deep learning models to adversarial attacks has shown the ease of designing samples to mislead a model into making incorrect predictions. In this work, we propose an explainability-based method for the accurate detection of adversarial samples on two datasets with different complexity and properties: Electronic Health Record (EHR) and chest X-ray (CXR) data. On the MIMIC-III and Henan-Renmin EHR datasets, we report a detection accuracy of 77% against the Longitudinal Adversarial Attack. On the MIMIC-CXR dataset, we achieve an accuracy of 88%; significantly improving on the state of the art of adversarial detection in both datasets by over 10% in all settings. We propose an anomaly detection based method using explainability techniques to detect adversarial samples which is able to generalise to different attack methods without a need for retraining.

Detecting Manipulated Facial Videos: A Time Series Solution

Zhang Zhewei, Ma Can, Gao Meilin, Ding Bowen
Track 2: Biometrics, Human Analysis and Behavior Understanding
Wed 13 Jan 2021 at 12:00 in session PS T2.2

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Auto-TLDR; Face-Alignment Based Bi-LSTM for Fake Video Detection

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We propose a new method to expose fake videos based on a time series solution. The method is based on bidirectional long short-term memory (Bi-LSTM) backbone architecture with two different types of features: {Face-Alignment} and {Dense-Face-Alignment}, in which both of them are physiological signals that can be distinguished between fake and original videos. We choose 68 landmark points as the feature of {Face-Alignment} and Pose Adaptive Feature (PAF) for {Dense-Face-Alignment}. Based on these two facial features, we designed two deep networks. In addition, we optimize our network by adding an attention mechanism that improves detection precision. Our method is tested over benchmarks of Face Forensics/Face Forensics++ dataset and show a promising performance on inference speed while maintaining accuracy with state-of art solutions that deal against DeepFake.

Triplet-Path Dilated Network for Detection and Segmentation of General Pathological Images

Jiaqi Luo, Zhicheng Zhao, Fei Su, Limei Guo
Track 5: Image and Signal Processing
Fri 15 Jan 2021 at 13:00 in session OS T5.5

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Auto-TLDR; Triplet-path Network for One-Stage Object Detection and Segmentation in Pathological Images

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Deep learning has been widely applied in the field of medical image processing. However, compared with flourishing visual tasks in natural images, the progress achieved in pathological images is not remarkable, and detection and segmentation, which are among basic tasks of computer vision, are regarded as two independent tasks. In this paper, we make full use of existing datasets and construct a triplet-path network using dilated convolutions to cooperatively accomplish one-stage object detection and nuclei segmentation for general pathological images. First, in order to meet the requirement of detection and segmentation, a novel structure called triplet feature generation (TFG) is designed to extract high-resolution and multiscale features, where features from different layers can be properly integrated. Second, considering that pathological datasets are usually small, a location-aware and partially truncated loss function is proposed to improve the classification accuracy of datasets with few images and widely varying targets. We compare the performance of both object detection and instance segmentation with state-of-the-art methods. Experimental results demonstrate the effectiveness and efficiency of the proposed network on two datasets collected from multiple organs.

Building Computationally Efficient and Well-Generalizing Person Re-Identification Models with Metric Learning

Vladislav Sovrasov, Dmitry Sidnev
Track 1: Artificial Intelligence, Machine Learning for Pattern Analysis
Fri 15 Jan 2021 at 15:00 in session OS T1.8

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Auto-TLDR; Cross-Domain Generalization in Person Re-identification using Omni-Scale Network

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This work considers the problem of domain shift in person re-identification.Being trained on one dataset, a re-identification model usually performs much worse on unseen data. Partially this gap is caused by the relatively small scale of person re-identification datasets (compared to face recognition ones, for instance), but it is also related to training objectives. We propose to use the metric learning objective, namely AM-Softmax loss, and some additional training practices to build well-generalizing, yet, computationally efficient models. We use recently proposed Omni-Scale Network (OSNet) architecture combined with several training tricks and architecture adjustments to obtain state-of-the art results in cross-domain generalization problem on a large-scale MSMT17 dataset in three setups: MSMT17-all->DukeMTMC, MSMT17-train->Market1501 and MSMT17-all->Market1501.

A Close Look at Deep Learning with Small Data

Lorenzo Brigato, Luca Iocchi
Track 1: Artificial Intelligence, Machine Learning for Pattern Analysis
Wed 13 Jan 2021 at 16:30 in session PS T1.8

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Auto-TLDR; Low-Complex Neural Networks for Small Data Conditions

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In this work, we perform a wide variety of experiments with different Deep Learning architectures in small data conditions. We show that model complexity is a critical factor when only a few samples per class are available. Differently from the literature, we improve the state of the art using low complexity models. We show that standard convolutional neural networks with relatively few parameters are effective in this scenario. In many of our experiments, low complexity models outperform state-of-the-art architectures. Moreover, we propose a novel network that uses an unsupervised loss to regularize its training. Such architecture either improves the results either performs comparably well to low capacity networks. Surprisingly, experiments show that the dynamic data augmentation pipeline is not beneficial in this particular domain. Statically augmenting the dataset might be a promising research direction while dropout maintains its role as a good regularizer.

Super-Resolution Guided Pore Detection for Fingerprint Recognition

Syeda Nyma Ferdous, Ali Dabouei, Jeremy Dawson, Nasser M. Nasarabadi
Track 2: Biometrics, Human Analysis and Behavior Understanding
Tue 12 Jan 2021 at 17:00 in session PS T2.1

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Auto-TLDR; Super-Resolution Generative Adversarial Network for Fingerprint Recognition Using Pore Features

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Performance of fingerprint recognition algorithms substantially rely on fine features extracted from fingerprints. Apart from minutiae and ridge patterns, pore features have proven to be usable for fingerprint recognition. Although features from minutiae and ridge patterns are quite attainable from low-resolution images, using pore features is practical only if the fingerprint image is of high resolution which necessitates a model that enhances the image quality of the conventional 500 ppi legacy fingerprints preserving the fine details. To find a solution for recovering pore information from low-resolution fingerprints, we adopt a joint learning-based approach that combines both super-resolution and pore detection networks. Our modified single image Super-Resolution Generative Adversarial Network (SRGAN) framework helps to reliably reconstruct high-resolution fingerprint samples from low-resolution ones assisting the pore detection network to identify pores with a high accuracy. The network jointly learns a distinctive feature representation from a real low-resolution fingerprint sample and successfully synthesizes a high-resolution sample from it. To add discriminative information and uniqueness for all the subjects, we have integrated features extracted from a deep fingerprint verifier with the SRGAN quality discriminator. We also add ridge reconstruction loss, utilizing ridge patterns to make the best use of extracted features. Our proposed method solves the recognition problem by improving the quality of fingerprint images. High recognition accuracy of the synthesized samples that is close to the accuracy achieved using the original high-resolution images validate the effectiveness of our proposed model.

Malware Detection by Exploiting Deep Learning over Binary Programs

Panpan Qi, Zhaoqi Zhang, Wei Wang, Chang Yao
Track 1: Artificial Intelligence, Machine Learning for Pattern Analysis
Thu 14 Jan 2021 at 12:00 in session PS T1.10

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Auto-TLDR; End-to-End Malware Detection without Feature Engineering

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Malware evolves rapidly over time, which makes existing solutions being ineffective in detecting newly released malware. Machine learning models that can learn to capture malicious patterns directly from the data play an increasingly important role in malware analysis. However, traditional machine learning models heavily depend on feature engineering. The extracted static features are vulnerable as hackers could create new malware with different feature values to deceive the machine learning models. In this paper, we propose an end-to-end malware detection framework consisting of convolutional neural network, autoencoder and neural decision trees. It learns the features from multiple domains for malware detection without feature engineering. In addition, since anti-virus products should have a very low false alarm rate to avoid annoying users, we propose a special loss function, which optimizes the recall for a fixed low false positive rate (e.g., less than 0.1%). Experiments show that the proposed framework has achieved a better recall than the baseline models, and the derived loss function also makes a difference.

An Experimental Evaluation of Recent Face Recognition Losses for Deepfake Detection

Yu-Cheng Liu, Chia-Ming Chang, I-Hsuan Chen, Yu Ju Ku, Jun-Cheng Chen
Track 2: Biometrics, Human Analysis and Behavior Understanding
Thu 14 Jan 2021 at 12:00 in session PS T2.4

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Auto-TLDR; Deepfake Classification and Detection using Loss Functions for Face Recognition

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Due to the recent breakthroughs of deep generative models, the fake faces, also known as deepfake which has been abused to deceive the general public, can be easily produced at scale and in very high fidelity. Many works focus on exploring various network architectures or various artifacts produced by deep generative models. Instead, in this work, we focus on the loss functions which have been shown to play a significant role in the context of face recognition. We perform a thorough study of several recent state-of-the-art losses commonly used in face recognition task for deepfake classification and detection since the current deepfake is highly related to face generation. With extensive experiments on the challenging FaceForensic++ and Celeb-DF datasets, the evaluation results provide a clear overview of the performance comparisons of different loss functions and generalization capability across different deepfake data.

Task-based Focal Loss for Adversarially Robust Meta-Learning

Yufan Hou, Lixin Zou, Weidong Liu
Track 1: Artificial Intelligence, Machine Learning for Pattern Analysis
Fri 15 Jan 2021 at 15:00 in session PS T1.14

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Auto-TLDR; Task-based Adversarial Focal Loss for Few-shot Meta-Learner

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Adversarial robustness of machine learning has been widely studied in recent years, and a series of effective methods are proposed to resist adversarial attacks. However, less attention is paid to few-shot meta-learners which are much more vulnerable due to the lack of training samples. In this paper, we propose Task-based Adversarial Focal Loss (TAFL) to handle this tough challenge on a typical meta-learner called MAML. More concretely, we regard few-shot classification tasks as normal samples in learning models and apply focal loss mechanism on them. Our proposed method focuses more on adversarially fragile tasks, leading to improvement on overall model robustness. Results of extensive experiments on several benchmarks demonstrate that TAFL can effectively promote the performance of the meta-learner on adversarial examples with elaborately designed perturbations.

The Application of Capsule Neural Network Based CNN for Speech Emotion Recognition

Xincheng Wen, Kunhong Liu
Track 5: Image and Signal Processing
Fri 15 Jan 2021 at 15:00 in session PS T5.7

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Auto-TLDR; CapCNN: A Capsule Neural Network for Speech Emotion Recognition

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Moreover, the abstraction of audio features makes it impossible to fully use the inherent relationship among audio features. This paper proposes a model that combines a convolutional neural network(CNN) and a capsule neural network (CapsNet), named as CapCNN. The advantage of CapCNN lies in that it provides a solution to solve time sensitivity and focus on the overall characteristics. In this study, it is found that CapCNN can well handle the speech emotion recognition task. Compared with other state-of-art methods, our algorithm shows high performances on the CASIA and EMODB datasets. The detailed analysis confirms that our method provides balanced results on the various classes.

Energy Minimum Regularization in Continual Learning

Xiaobin Li, Weiqiang Wang
Track 1: Artificial Intelligence, Machine Learning for Pattern Analysis
Thu 14 Jan 2021 at 16:00 in session PS T1.12

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Auto-TLDR; Energy Minimization Regularization for Continuous Learning

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How to give agents the ability of continuous learning like human and animals is still a challenge. In the regularized continual learning method OWM, the constraint of the model on the energy compression of the learned task is ignored, which results in the poor performance of the method on the dataset with a large number of learning tasks. In this paper, we propose an energy minimization regularization(EMR) method to constrain the energy of learned tasks, providing enough learning space for the following tasks that are not learned, and increasing the capacity of the model to the number of learning tasks. A large number of experiments show that our method can effectively increase the capacity of the model and reduce the sensitivity of the model to the number of tasks and the size of the network.

Level Three Synthetic Fingerprint Generation

Andre Wyzykowski, Mauricio Pamplona Segundo, Rubisley Lemes
Track 2: Biometrics, Human Analysis and Behavior Understanding
Tue 12 Jan 2021 at 17:00 in session PS T2.1

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Auto-TLDR; Synthesis of High-Resolution Fingerprints with Pore Detection Using CycleGAN

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Today's legal restrictions that protect the privacy of biometric data are hampering fingerprint recognition researches. For instance, all high-resolution fingerprint databases ceased to be publicly available. To address this problem, we present a novel hybrid approach to synthesize realistic, high-resolution fingerprints. First, we improved Anguli, a handcrafted fingerprint generator, to obtain dynamic ridge maps with sweat pores and scratches. Then, we trained a CycleGAN to transform these maps into realistic fingerprints. Unlike other CNN-based works, we can generate several images for the same identity. We used our approach to create a synthetic database with 7400 images in an attempt to propel further studies in this field without raising legal issues. We included sweat pore annotations in 740 images to encourage research developments in pore detection. In our experiments, we employed two fingerprint matching approaches to confirm that real and synthetic databases have similar performance. We conducted a human perception analysis where sixty volunteers could hardly differ between real and synthesized fingerprints. Given that we also favorably compare our results with the most advanced works in the literature, our experimentation suggests that our approach is the new state-of-the-art.

Evaluation of Anomaly Detection Algorithms for the Real-World Applications

Marija Ivanovska, Domen Tabernik, Danijel Skocaj, Janez Pers
Track 1: Artificial Intelligence, Machine Learning for Pattern Analysis
Thu 14 Jan 2021 at 14:00 in session PS T1.11

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Auto-TLDR; Evaluating Anomaly Detection Algorithms for Practical Applications

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Anomaly detection in complex data structures is oneof the most challenging problems in computer vision. In manyreal-world problems, for example in the quality control in modernmanufacturing, the anomalous samples are usually rare, resultingin (highly) imbalanced datasets. However, in current researchpractice, these scenarios are rarely modeled, and as a conse-quence, evaluation of anomaly detection algorithms often do notreproduce results that are useful for practical applications. First,even in case of highly unbalanced input data, anomaly detectionalgorithms are expected to significantly reduce the proportionof anomalous samples, detecting ”almost all” anomalous samples(with exact specifications depending on the target customer). Thisplaces high importance on only the small part of the ROC curve,possibly rendering the standard metrics such as AUC (AreaUnder Curve) and AP (Average Precision) useless. Second, thetarget of automatic anomaly detection in practical applicationsis significant reduction in manual work required, and standardmetrics are poor predictor of this feature. Finally, the evaluationmay produce erratic results for different randomly initializedtraining runs of the neural network, producing evaluation resultsthat may not reproduce well in practice. In this paper, we presentan evaluation methodology that avoids these pitfalls.

Cut and Compare: End-To-End Offline Signature Verification Network

Xi Lu, Lin-Lin Huang, Fei Yin
Track 4: Document and Media Analysis
Wed 13 Jan 2021 at 12:00 in session PS T4.2

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Auto-TLDR; An End-to-End Cut-and-Compare Network for Offline Signature Verification

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Offline signature verification, to determine whether a handwritten signature image is genuine or forged for a claimed identity, is needed in many applications. How to extract salient features and how to calculate similarity scores are the major issues. In this paper, we propose a novel end-to-end cut-and-compare network for offline signature verification. Based on the Spatial Transformer Network (STN), discriminative regions are segmented from a pair of input signature images and are compared attentively with help of Attentive Recurrent Comparator (ARC). An adaptive distance fusion module is proposed to fuse the distances of these regions. To address the intrapersonal variability problem, we design a smoothed double-margin loss to train the network. The proposed network achieves state-of-the-art performance on CEDAR, GPDS Synthetic, BHSig-H and BHSig-B datasets of different languages. Furthermore, our network shows strong generalization ability on cross-language test.

Which are the factors affecting the performance of audio surveillance systems?

Antonio Greco, Antonio Roberto, Alessia Saggese, Mario Vento
Track 5: Image and Signal Processing
Thu 14 Jan 2021 at 14:00 in session OS T5.4

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Auto-TLDR; Sound Event Recognition Using Convolutional Neural Networks and Visual Representations on MIVIA Audio Events

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Sound event recognition systems are rapidly becoming part of our life, since they can be profitably used in several vertical markets, ranging from audio security applications to scene classification and multi-modal analysis in social robotics. In the last years, a not negligible part of the scientific community started to apply Convolutional Neural Networks (CNNs) to image-based representations of the audio stream, due to their successful adoption in almost all the computer vision tasks. In this paper, we carry out a detailed benchmark of various widely used CNN architectures and visual representations on a popular dataset, namely the MIVIA Audio Events database. Our analysis is aimed at understanding how these factors affect the sound event recognition performance with a particular focus on the false positive rate, very relevant in audio surveillance solutions. In fact, although most of the proposed solutions achieve a high recognition rate, the capability of distinguishing the events-of-interest from the background is often not yet sufficient for real systems, and prevent its usage in real applications. Our comprehensive experimental analysis investigates this aspect and allows to identify useful design guidelines for increasing the specificity of sound event recognition systems.

Defense Mechanism against Adversarial Attacks Using Density-Based Representation of Images

Yen-Ting Huang, Wen-Hung Liao, Chen-Wei Huang
Track 1: Artificial Intelligence, Machine Learning for Pattern Analysis
Wed 13 Jan 2021 at 12:00 in session PS T1.3

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Auto-TLDR; Adversarial Attacks Reduction Using Input Recharacterization

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Adversarial examples are slightly modified inputs devised to cause erroneous inference of deep learning models. Protection against the intervention of adversarial examples is a fundamental issue that needs to be addressed before the wide adoption of deep-learning based intelligent systems. In this research, we utilize the method known as input recharacterization to effectively eliminate the perturbations found in the adversarial examples. By converting images from the intensity domain into density-based representation using halftoning operation, performance of the classifier can be properly maintained. With adversarial attacks generated using FGSM, I-FGSM, and PGD, the top-5 accuracy of the hybrid model can still achieve 80.97%, 78.77%, 81.56%, respectively. Although the accuracy has been slightly affected, the influence of adversarial examples is significantly discounted. The average improvement over existing input transform defense mechanisms is approximately 10%.

MRP-Net: A Light Multiple Region Perception Neural Network for Multi-Label AU Detection

Yang Tang, Shuang Chen, Honggang Zhang, Gang Wang, Rui Yang
Track 2: Biometrics, Human Analysis and Behavior Understanding
Wed 13 Jan 2021 at 12:00 in session PS T2.2

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Auto-TLDR; MRP-Net: A Fast and Light Neural Network for Facial Action Unit Detection

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Facial Action Units (AUs) are of great significance in communication. Automatic AU detection can improve the understanding of psychological condition and emotional status. Recently, a number of deep learning methods have been proposed to take charge with problems in automatic AU detection. Several challenges, like unbalanced labels and ignorance of local information, remain to be addressed. In this paper, we propose a fast and light neural network called MRP-Net, which is an end-to-end trainable method for facial AU detection to solve these problems. First, we design a Multiple Region Perception (MRP) module aimed at capturing different locations and sizes of features in the deeper level of the network without facial landmark points. Then, in order to balance the positive and negative samples in the large dataset, a batch balanced method adjusting the weight of every sample in one batch in our loss function is suggested. Experimental results on two popular AU datasets, BP4D and DISFA prove that MRP-Net outperforms state-of-the-art methods. Compared with the best method, not only does MRP-Net have an average F1 score improvement of 2.95% on BP4D and 5.43% on DISFA, and it also decreases the number of network parameters by 54.62% and the number of network FLOPs by 19.6%.

Color, Edge, and Pixel-Wise Explanation of Predictions Based onInterpretable Neural Network Model

Jay Hoon Jung, Youngmin Kwon
Track 1: Artificial Intelligence, Machine Learning for Pattern Analysis
Wed 13 Jan 2021 at 12:00 in session PS T1.3

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Auto-TLDR; Explainable Deep Neural Network with Edge Detecting Filters

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We design an interpretable network model by introducing explainable components into a Deep Neural Network (DNN). We substituted the first kernels of a Convolutional Neural Network (CNN) and a ResNet-50 with the well-known edge detecting filters such as Sobel, Prewitt, and other filters. Each filters' relative importance scores are measured with a variant of Layer-wise Relevance Propagation (LRP) method proposed by Bach et al. Since the effects of the edge detecting filters are well understood, our model provides three different scores to explain individual predictions: the scores with respect to (1) colors, (2) edge filters, and (3) pixels of the image. Our method provides more tools to analyze the predictions by highlighting the location of important edges and colors in the images. Furthermore, the general features of a category can be shown in our scores as well as individual predictions. At the same time, the model does not degrade performances on MNIST, Fruit360 and ImageNet datasets.

Motion Complementary Network for Efficient Action Recognition

Ke Cheng, Yifan Zhang, Chenghua Li, Jian Cheng, Hanqing Lu
Track 2: Biometrics, Human Analysis and Behavior Understanding
Wed 13 Jan 2021 at 12:00 in session PS T2.2

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Auto-TLDR; Efficient Motion Complementary Network for Action Recognition

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Both two-stream ConvNet and 3D ConvNet are widely used in action recognition. However, both methods are not efficient for deployment: calculating optical flow is very slow, while 3D convolution is computationally expensive. Our key insight is that the motion information from optical flow maps is complementary to the motion information from 3D ConvNet. Instead of simply combining these two methods, we propose two novel techniques to enhance the performance with less computational cost: \textit{fixed-motion-accumulation} and \textit{balanced-motion-policy}. With these two techniques, we propose a novel framework called Efficient Motion Complementary Network(EMC-Net) that enjoys both high efficiency and high performance. We conduct extensive experiments on Kinetics, UCF101, and Jester datasets. We achieve notably higher performance while consuming 4.7$\times$ less computation than I3D, 11.6$\times$ less computation than ECO, 17.8$\times$ less computation than R(2+1)D. On Kinetics dataset, we achieve 2.6\% better performance than the recent proposed TSM with 1.4$\times$ fewer FLOPs and 10ms faster on K80 GPU.

Planar 3D Transfer Learning for End to End Unimodal MRI Unbalanced Data Segmentation

Martin Kolarik, Radim Burget, Carlos M. Travieso-Gonzalez, Jan Kocica
Track 1: Artificial Intelligence, Machine Learning for Pattern Analysis
Thu 14 Jan 2021 at 16:00 in session OS T1.6

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Auto-TLDR; Planar 3D Res-U-Net Network for Unbalanced 3D Image Segmentation using Fluid Attenuation Inversion Recover

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We present a novel approach of 2D to 3D transfer learning based on mapping pre-trained 2D convolutional neural network weights into planar 3D kernels. The method is validated by proposed planar 3D res-u-net network with encoder transferred from the 2D VGG-16 which is applied for a single-stage unbalanced 3D image data segmentation. In particular, we evaluate the method on the MICCAI 2016 MS lesion segmentation challenge dataset utilizing solely Fluid Attenuation Inversion Recover (FLAIR) sequence without brain extraction for training and inference to simulate real medical praxis. The planar 3D res-u-net network performed the best both in sensitivity and Dice score amongst end to end methods processing raw MRI scans and achieved comparable Dice score to a state-of-the-art unimodal not end to end approach. Complete source code was released under the open-source license and this paper is in compliance with the Machine learning Reproducibility Checklist. By implementing practical transfer learning for 3D data representation we were able to successfully segment heavily unbalanced data without selective sampling and achieved more reliable results using less training data in single modality. From medical perspective, the unimodal approach gives an advantage in real praxis as it does not require co-registration nor additional scanning time during examination. Although modern medical imaging methods capture high resolution 3D anatomy scans suitable for computer aided detection system processing, deployment of automatic systems for interpretation of radiology imaging is still rather theoretical in many medical areas. Our work aims to bridge the gap offering solution for partial research questions.

Viability of Optical Coherence Tomography for Iris Presentation Attack Detection

Renu Sharma, Arun Ross
Track 2: Biometrics, Human Analysis and Behavior Understanding
Tue 12 Jan 2021 at 17:00 in session PS T2.1

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Auto-TLDR; Optical Coherence Tomography Imaging for Iris Presentation Attack Detection

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In this paper, we first propose the use of Optical Coherence Tomography (OCT) imaging for the problem of iris presentation attack (PA) detection. Secondly, we assess its viability by comparing its performance with respect to traditional modalities, viz., near-infrared (NIR) and visible spectrum. OCT imaging provides a cross-sectional view of an eye, whereas NIR and visible spectrum imaging provide 2D iris textural information. Implementation is performed using three state-of-the-art deep architectures (VGG19, ResNet50 and DenseNet121) to differentiate between bonafide and PA samples for each of the three imaging modalities. Experiments are performed on a dataset of 2,169 bonafide, 177 Van Dyke eyes and 360 cosmetic contact images acquired using all three imaging modalities under intra-attack (known PAs) and cross-attack (unknown PAs) scenario. We observe promising results demonstrating OCT as a viable solution for iris PA detection.

One-Shot Learning for Acoustic Identification of Bird Species in Non-Stationary Environments

Michelangelo Acconcjaioco, Stavros Ntalampiras
Track 5: Image and Signal Processing
Wed 13 Jan 2021 at 16:30 in session PS T5.4

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Auto-TLDR; One-shot Learning in the Bioacoustics Domain using Siamese Neural Networks

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This work introduces the one-shot learning paradigm in the computational bioacoustics domain. Even though, most of the related literature assumes availability of data characterizing the entire class dictionary of the problem at hand, that is rarely true as a habitat's species composition is only known up to a certain extent. Thus, the problem needs to be addressed by methodologies able to cope with non-stationarity. To this end, we propose a framework able to detect changes in the class dictionary and incorporate new classes on the fly. We design an one-shot learning architecture composed of a Siamese Neural Network operating in the logMel spectrogram space. We extensively examine the proposed approach on two datasets of various bird species using suitable figures of merit. Interestingly, such a learning scheme exhibits state of the art performance, while taking into account extreme non-stationarity cases.