In the front-end domain , much research has been conducted on finding effective features which can distinguish spoofed speech from genuine speech and the published results show that dynamic acoustic features work more effectively than static ones . In the back-end domain , Gaussian mixture model ( GMM ) and deep neural networks ( DNNs ) are the two most popular types of classifiers used for spoofing detection . The log-likelihood ratios ( LLRs ) generated by the difference of human and spoofing log-likelihoods are used as spoofing detection scores . During the post-challenge study , we further investigate the use of deep features that enhances the discriminative ability between genuine and spoofed speech . In this paper , we summarize the findings from the perspective of long range acoustic and deep features for spoof detection . We make a comprehensive analysis on the nature of different kinds of spoofing attacks and system development . In this paper we focus on face spoofing detection . The majority of papers on spoofing attack detection formulate the problem as a two or multiclass learning task , attempting to separate normal accesses from samples of different types of spoofing attacks . In this paper we adopt the anomaly detection approach proposed in [ 1 ] , where the detector is trained on genuine accesses only using one-class classifiers and investigate the merit of subject specific solutions . We evaluate their robustness against realistic spoof attacks obtained by fabricating fake biometric traits . The main goal of our study is to investigate whether a realistic spoof attack against both modalities can allow the attacker to crack the multimodal system . Our results show that even in a realistic , non-worst case scenario , the false acceptance rate ( FAR ) can remarkably increase . Hence , we present a novel approach based on analyzing facial image quality and face image texture for detecting whether there is a live person or a fake person . The proposed approach analyzes the spoof face detection based on video and printout image . Two different feature is extracted ( chromatic moment and Local Binary Pattern ( LBP ) ) from the face image . Face spoofing detection nowadays has attracted attentions regarding the biometrics authentication issue . Inspired by the observation that face spoofing detection is highly relevant with the inherent image quality which also strongly depends on the properties of the capturing devices and conditions , in this paper , we tackle the spoofing detection problem based on a two-stage learning approach . Firstly , we manually cluster the training samples based on the prior knowledge of face sample quality ( e.g. camera model ) , and multiple quality-guided classifiers are trained based on each cluster with extracted image quality assessment ( IQA ) feature . Inter-database and intra-database experiments are incorporated into the evaluation scheme to account for the sensor interoperability problem . Second , a new and more realistic formulation of the spoofing detection problem based on the anomaly detection concept is proposed where the training data come from the positive class only . The test data , of course , may come from the positive or negative class . The synthesized textured contact lenses iris images are used for spoofing iris detection training to improve the robustness of classification algorithm . Both the Cycle-GAN and the 4DCycle-GAN synthesizing images can improve the spoof classification results . Moreover , by using the 4DCycle-GAN , the spoof classification results are distinctly improved for unrelated non-homologous database experiments . Among the single antenna detection/mitigation designs , some form of antenna movement over time is required . For example , the use of a synthetic aperture [ 4 ] and the generation of high frequency antenna motion [ 5 ] has been proposed for spoof detection/mitigation . All of the aforementioned innovations utilize additional signal processing blocks in the backend , in conjunction with the supplemented frontend hardware , to achieve their intended goals . We will also examine the raw GNSS measurements generated by the phone and combine those into a solution that explores the potential of the sum of squares residuals , which will add protection levels for the GNSS navigation solution . In addition , we will also look into the fused position algorithm of the framework and use those in the aid of the spoofing detection attack when cellular connection or Wi-Fi access points are available . Finally , we perform a direct comparison of the inertial sensors available inside the phone ( accelerometers and gyroscopes ) and use this as another set of measurements helping in the spoofing or RFI detection . However , the problem is more complex in practical scenario as the type of attack could be unseen to system designers . In this paper , we formulate the face presentation attack detection task under an open-set setting and address with our proposed deep anomaly detection based method . The training process is end-to-end supervised by a novel hypersphere loss function and the decision making is directly based on the learned feature representation . However , the security of this recognition method can be compromised by attackers ( unauthorized persons ) , who might bypass the recognition system using artificial facial images . In addition , most previous studies on face presentation attack detection have only utilized spatial information . To address this problem , we propose a visible-light camera sensor-based presentation attack detection that is based on both spatial and temporal information , using the deep features extracted by a stacked convolutional neural network (CNN)-recurrent neural network ( RNN ) along with handcrafted features . We also present the performance of existing presentation attack detection algorithms on the proposed MLFP database . It is observed that the thermal imaging spectrum is most effective in detecting face presentation attacks . Even though the field of Face Presentation Attack Detection ( PAD ) has been around for quite a long time , but still it is quite a new field to be implemented on smartphones . Implementation on smartphones is different because the limited computing power of the smartphones when compared to computers . In the second testbed configuration , we evaluated the performance of objective LSCI for complex fluid flow scenes . Finally , we report on the quantitative measurement of speckle contrast as a function of fluid flow rate , thereby demonstrating the use of optimized LSCI as an important sensing modality for the detection of presentation attacks in biometric authentication systems . With the growing number of voice biometrics applications ( e.g. banking ) , it has become necessary to be able to assess and compare different systems ' detection of Presentation Attacks , as attackers could have access to sensitive data . For this end , a common ground is needed to perform comparable security evaluations . Since the inferred pulse signal gives information on the liveness of the recorded subject , it can be used for biometric presentation attack detection ( PAD ) . Inspired by work made for speaker presentation attack detection , we propose to use long-term spectral statistical features of the pulse signal to discriminate real accesses from attack attempts . A thorough experimental evaluation , with different rPPG and classification algorithms is carried on four publicly available datasets containing a wide range of face presentation attacks . Digital face presentation attack detection via weighted local magnitude pattern . Advancements in smartphone applications have empowered even non-technical users to perform sophisticated operations such as morphing in faces as few tap operations . Ear recognition has received broad attention from the biometric community and its emerging usage in multiple applications is raising new security concerns , with robustness against presentation attacks being a very active field of research . This paper addresses for the first time the ear presentation attack detection problem by developing an exhaustive benchmarking study on the performance of state-of-the-art light field and non-light field based ear presentation attack detection solutions . In this context , this paper also proposes an appropriate ear artefact database captured with a Lytro ILLUM lenslet light field camera , including both 2D and light field contents , using several types of presentation attack instruments , including laptop , tablet and two different mobile phones . State-of-the-art solutions are assessed in terms of accuracy , generalization and complexity , using a common , representative evaluation framework . This paper also proposes a novel face presentation attack detection solution , based on a histogram of oriented gradients descriptor , which exploits the disparity information available in light field imaging . The evaluation of the proposed face presentation attack detection solution for different presentation attack types shows a very effective and stable performance , notably performing better than the state-of-the-art alternatives . Can forensic traces and classification features be learned directly from data ? Recently , researchers have shown significant interest in using techniques from deep learning to address problems in multimedia security such as image manipulation detection and steganalysis [ 1 , 21 ] . Tools such as convolutional neural networks ( CNNs ) [ 17 ] show particular promise due to their ability to adaptively learn decision features from large sets of data . A large number of visitors , simplicity of publishing information as well as high level of trust in the posted information create an environment vulnerable to mind manipulation processes . This research is devoted to the development of the system for detecting manipulation in online discussions . Considering peculiarities and tendencies of communication in online discussions , the architecture of the system for detecting manipulation was designed . However , a major concern for us remains fraudulent manipulation of images that misrepresents the results of experiments . New software has become available that permits detection of image manipulation and is now being routinely and uniformly used in the evaluation of all figures from articles nearing acceptance at ATS journals , including AJRCCM , AJRCMB , and PATS ( 4 , 6 ) . The Editors are informed of all instances of image manipulation and will contact authors for explanation before publication if we have concerns that their data are not being impartially presented . We propose a new and effective adaptation method for one state-of-the-art image manipulation detection pipeline , and we call our proposed method Gaussian mixture model Resizing Adaptation by Fine-Tuning ( GRAFT ) . Adaptation is performed in an unsupervised fashion , i.e. , without using any ground-truth label in the pre-resized testing domain , for the detection of image manipulation on very small patches . Experimental results show that the proposed GRAFT method can effectively improve the detection accuracy in this challenging scenario of unsupervised adaptation to resizing pre-processing . However these approaches have been shown to have limitations . Since prevention of manipulation seems to be elusive , an interesting research direction therefore is detection of manipulation . Motivated by this , we initiate the study of detection of possible manipulators in an election . Yet , there are not a single satisfactory solution to prevent manipulation . In such a scenario where prevention fails even after considerable research effort of more than four decades , a natural research direction is to explore detection of manipulation . This is precisely the goal of one of our works [ 2 ] in this thesis . The conclusions drawn from the findings suggest that there exists specific tests that can indicate an altered image . DETECTING IMAGE MANIPULATIONS WITHOUT THE ORIGINAL Detecting image manipulations in the presence of JPEG post-processing is often a challenging task . For this reason , in many cases , JPEG compression is applied as a laundering-type counter-forensic attack , to impair the performance of the detector . However , anyone can easily edit or manipulate any digital image on a computer . Thus , an effective method for detecting digital image manipulation is required for retaining authenticity . Image-manipulation detection is applied in investigations of crimes and photographic evidence . The spread of misinformation through synthetically generated yet realistic images and videos has become a significant problem , calling for robust manipulation detection methods . Despite the predominant effort of detecting face manipulation in still images , less attention has been paid to the identification of tampered faces in videos by taking advantage of the temporal information present in the stream . Recurrent convolutional models are a class of deep learning models which have proven effective at exploiting the temporal information from image streams across domains . Learning unknown objects in the environment is important for detection and manipulation tasks . Prior to learning the unknown objects the ground-truth labels have to be provided . In such situations , finger dorsal images can be thought of as an alternative which can be captured without much user cooperation and are more appropriate for outdoor security applications . In this paper , we present a first feasibility study of spoofing attack scenarios on finger dorsal authentication system , which include four types of presentation attacks such as printed paper , wrapped printed paper , scan and mobile . This study also presents a CNN based spoofing attack detection method which employ state-of-the-art deep learning techniques along with transfer learning mechanism .
In the front-end domain , much research has been conducted on finding effective features which can distinguish spoofed speech from genuine speech and the published results show that dynamic acoustic features work more effectively than static ones . In the back-end domain , Gaussian mixture model ( GMM ) and deep neural networks ( DNNs ) are the two most popular types of classifiers used for spoofing detection . The log-likelihood ratios ( LLRs ) generated by the difference of human and spoofing log-likelihoods are used as spoofing detection scores .
During the post-challenge study , we further investigate the use of deep features that enhances the discriminative ability between genuine and spoofed speech . In this paper , we summarize the findings from the perspective of long range acoustic and deep features for spoof detection . We make a comprehensive analysis on the nature of different kinds of spoofing attacks and system development .
In this paper we focus on face spoofing detection . The majority of papers on spoofing attack detection formulate the problem as a two or multiclass learning task , attempting to separate normal accesses from samples of different types of spoofing attacks . In this paper we adopt the anomaly detection approach proposed in [ 1 ] , where the detector is trained on genuine accesses only using one-class classifiers and investigate the merit of subject specific solutions .
We evaluate their robustness against realistic spoof attacks obtained by fabricating fake biometric traits . The main goal of our study is to investigate whether a realistic spoof attack against both modalities can allow the attacker to crack the multimodal system . Our results show that even in a realistic , non-worst case scenario , the false acceptance rate ( FAR ) can remarkably increase .
Hence , we present a novel approach based on analyzing facial image quality and face image texture for detecting whether there is a live person or a fake person . The proposed approach analyzes the spoof face detection based on video and printout image . Two different feature is extracted ( chromatic moment and Local Binary Pattern ( LBP ) ) from the face image .
Face spoofing detection nowadays has attracted attentions regarding the biometrics authentication issue . Inspired by the observation that face spoofing detection is highly relevant with the inherent image quality which also strongly depends on the properties of the capturing devices and conditions , in this paper , we tackle the spoofing detection problem based on a two-stage learning approach . Firstly , we manually cluster the training samples based on the prior knowledge of face sample quality ( e.g. camera model ) , and multiple quality-guided classifiers are trained based on each cluster with extracted image quality assessment ( IQA ) feature .
Inter-database and intra-database experiments are incorporated into the evaluation scheme to account for the sensor interoperability problem . Second , a new and more realistic formulation of the spoofing detection problem based on the anomaly detection concept is proposed where the training data come from the positive class only . The test data , of course , may come from the positive or negative class .
The synthesized textured contact lenses iris images are used for spoofing iris detection training to improve the robustness of classification algorithm . Both the Cycle-GAN and the 4DCycle-GAN synthesizing images can improve the spoof classification results . Moreover , by using the 4DCycle-GAN , the spoof classification results are distinctly improved for unrelated non-homologous database experiments .
Among the single antenna detection/mitigation designs , some form of antenna movement over time is required . For example , the use of a synthetic aperture [ 4 ] and the generation of high frequency antenna motion [ 5 ] has been proposed for spoof detection/mitigation . All of the aforementioned innovations utilize additional signal processing blocks in the backend , in conjunction with the supplemented frontend hardware , to achieve their intended goals .
We will also examine the raw GNSS measurements generated by the phone and combine those into a solution that explores the potential of the sum of squares residuals , which will add protection levels for the GNSS navigation solution . In addition , we will also look into the fused position algorithm of the framework and use those in the aid of the spoofing detection attack when cellular connection or Wi-Fi access points are available . Finally , we perform a direct comparison of the inertial sensors available inside the phone ( accelerometers and gyroscopes ) and use this as another set of measurements helping in the spoofing or RFI detection .
However , the problem is more complex in practical scenario as the type of attack could be unseen to system designers . In this paper , we formulate the face presentation attack detection task under an open-set setting and address with our proposed deep anomaly detection based method . The training process is end-to-end supervised by a novel hypersphere loss function and the decision making is directly based on the learned feature representation .
However , the security of this recognition method can be compromised by attackers ( unauthorized persons ) , who might bypass the recognition system using artificial facial images . In addition , most previous studies on face presentation attack detection have only utilized spatial information . To address this problem , we propose a visible-light camera sensor-based presentation attack detection that is based on both spatial and temporal information , using the deep features extracted by a stacked convolutional neural network (CNN)-recurrent neural network ( RNN ) along with handcrafted features .
We also present the performance of existing presentation attack detection algorithms on the proposed MLFP database . It is observed that the thermal imaging spectrum is most effective in detecting face presentation attacks .
Even though the field of Face Presentation Attack Detection ( PAD ) has been around for quite a long time , but still it is quite a new field to be implemented on smartphones . Implementation on smartphones is different because the limited computing power of the smartphones when compared to computers .
In the second testbed configuration , we evaluated the performance of objective LSCI for complex fluid flow scenes . Finally , we report on the quantitative measurement of speckle contrast as a function of fluid flow rate , thereby demonstrating the use of optimized LSCI as an important sensing modality for the detection of presentation attacks in biometric authentication systems .
With the growing number of voice biometrics applications ( e.g. banking ) , it has become necessary to be able to assess and compare different systems ' detection of Presentation Attacks , as attackers could have access to sensitive data . For this end , a common ground is needed to perform comparable security evaluations .
Since the inferred pulse signal gives information on the liveness of the recorded subject , it can be used for biometric presentation attack detection ( PAD ) . Inspired by work made for speaker presentation attack detection , we propose to use long-term spectral statistical features of the pulse signal to discriminate real accesses from attack attempts . A thorough experimental evaluation , with different rPPG and classification algorithms is carried on four publicly available datasets containing a wide range of face presentation attacks .
Digital face presentation attack detection via weighted local magnitude pattern . Advancements in smartphone applications have empowered even non-technical users to perform sophisticated operations such as morphing in faces as few tap operations .
Ear recognition has received broad attention from the biometric community and its emerging usage in multiple applications is raising new security concerns , with robustness against presentation attacks being a very active field of research . This paper addresses for the first time the ear presentation attack detection problem by developing an exhaustive benchmarking study on the performance of state-of-the-art light field and non-light field based ear presentation attack detection solutions . In this context , this paper also proposes an appropriate ear artefact database captured with a Lytro ILLUM lenslet light field camera , including both 2D and light field contents , using several types of presentation attack instruments , including laptop , tablet and two different mobile phones .
State-of-the-art solutions are assessed in terms of accuracy , generalization and complexity , using a common , representative evaluation framework . This paper also proposes a novel face presentation attack detection solution , based on a histogram of oriented gradients descriptor , which exploits the disparity information available in light field imaging . The evaluation of the proposed face presentation attack detection solution for different presentation attack types shows a very effective and stable performance , notably performing better than the state-of-the-art alternatives .
Can forensic traces and classification features be learned directly from data ? Recently , researchers have shown significant interest in using techniques from deep learning to address problems in multimedia security such as image manipulation detection and steganalysis [ 1 , 21 ] . Tools such as convolutional neural networks ( CNNs ) [ 17 ] show particular promise due to their ability to adaptively learn decision features from large sets of data .
A large number of visitors , simplicity of publishing information as well as high level of trust in the posted information create an environment vulnerable to mind manipulation processes . This research is devoted to the development of the system for detecting manipulation in online discussions . Considering peculiarities and tendencies of communication in online discussions , the architecture of the system for detecting manipulation was designed .
However , a major concern for us remains fraudulent manipulation of images that misrepresents the results of experiments . New software has become available that permits detection of image manipulation and is now being routinely and uniformly used in the evaluation of all figures from articles nearing acceptance at ATS journals , including AJRCCM , AJRCMB , and PATS ( 4 , 6 ) . The Editors are informed of all instances of image manipulation and will contact authors for explanation before publication if we have concerns that their data are not being impartially presented .
We propose a new and effective adaptation method for one state-of-the-art image manipulation detection pipeline , and we call our proposed method Gaussian mixture model Resizing Adaptation by Fine-Tuning ( GRAFT ) . Adaptation is performed in an unsupervised fashion , i.e. , without using any ground-truth label in the pre-resized testing domain , for the detection of image manipulation on very small patches . Experimental results show that the proposed GRAFT method can effectively improve the detection accuracy in this challenging scenario of unsupervised adaptation to resizing pre-processing .
However these approaches have been shown to have limitations . Since prevention of manipulation seems to be elusive , an interesting research direction therefore is detection of manipulation . Motivated by this , we initiate the study of detection of possible manipulators in an election .
Yet , there are not a single satisfactory solution to prevent manipulation . In such a scenario where prevention fails even after considerable research effort of more than four decades , a natural research direction is to explore detection of manipulation . This is precisely the goal of one of our works [ 2 ] in this thesis .
The conclusions drawn from the findings suggest that there exists specific tests that can indicate an altered image . DETECTING IMAGE MANIPULATIONS WITHOUT THE ORIGINAL
Detecting image manipulations in the presence of JPEG post-processing is often a challenging task . For this reason , in many cases , JPEG compression is applied as a laundering-type counter-forensic attack , to impair the performance of the detector .
However , anyone can easily edit or manipulate any digital image on a computer . Thus , an effective method for detecting digital image manipulation is required for retaining authenticity . Image-manipulation detection is applied in investigations of crimes and photographic evidence .
The spread of misinformation through synthetically generated yet realistic images and videos has become a significant problem , calling for robust manipulation detection methods . Despite the predominant effort of detecting face manipulation in still images , less attention has been paid to the identification of tampered faces in videos by taking advantage of the temporal information present in the stream . Recurrent convolutional models are a class of deep learning models which have proven effective at exploiting the temporal information from image streams across domains .
Learning unknown objects in the environment is important for detection and manipulation tasks . Prior to learning the unknown objects the ground-truth labels have to be provided .
In such situations , finger dorsal images can be thought of as an alternative which can be captured without much user cooperation and are more appropriate for outdoor security applications . In this paper , we present a first feasibility study of spoofing attack scenarios on finger dorsal authentication system , which include four types of presentation attacks such as printed paper , wrapped printed paper , scan and mobile . This study also presents a CNN based spoofing attack detection method which employ state-of-the-art deep learning techniques along with transfer learning mechanism .
Spoofing Detection in Automatic Speaker Verification Systems Using DNN Classifiers and Dynamic Acoustic Features Long Range Acoustic and Deep Features Perspective on ASVspoof 2019 Spoofing Attack Detection by Anomaly Detection Robustness of multi-modal biometric systems under realistic spoof attacks against all traits REAL TIME FACE LIVENESS DETECTION WITH IMAGE QUALITY AND TEXTURE PARAMETER Face spoofing detection with image quality regression An Anomaly Detection Approach to Face Spoofing Detection: A New Formulation and Evaluation Protocol Generation Textured Contact Lenses Iris Images Based on 4DCycle-GAN Single Antenna GPS Spoof Detection that is Simple, Static, Instantaneous and Backwards Compatible for Aerial Applications Android Raw GNSS Measurements as the New Anti-Spoofing and Anti-Jamming Solution Unseen Face Presentation Attack Detection with Hypersphere Loss Visible-Light Camera Sensor-Based Presentation Attack Detection for Face Recognition by Combining Spatial and Temporal Information Face Presentation Attack with Latex Masks in Multispectral Videos Presentation Attack Detection for Face Recognition on Smartphones: A Comprehensive Review Quantitative laser speckle contrast imaging for presentation attack detection in biometric authentication systems Performing a Presentation Attack Detection on Voice Biometrics Remote Blood Pulse Analysis for Face Presentation Attack Detection SWAPPED! Digital face presentation attack detection via weighted local magnitude pattern Ear Presentation Attack Detection: Benchmarking Study with First Lenslet Light Field Database Light Field-Based Face Presentation Attack Detection: Reviewing, Benchmarking and One Step Further Design Principles of Convolutional Neural Networks for Multimedia Forensics Development of the System for Detecting Manipulation in Online Discussions The ATS Journals' policy on image manipulation. GRAFT: Unsupervised Adaptation to Resizing for Detection of Image Manipulation Detecting Possible Manipulators in Elections Computational Complexity of Fundamental Problems in Social Choice Theory Detecting image manipulations without the original Adversarial CNN Training Under JPEG Laundering Attacks: a Game-Theoretic Approach Structuring Element-counting Approach for Morphological Pattern Spectrum-based Image Manipulation Detection Recurrent Convolutional Strategies for Face Manipulation Detection in Videos Learning to Segment Generic Handheld Objects Using Class-Agnostic Deep Comparison and Segmentation Network FDSNet: Finger dorsal image spoof detection network using light field camera
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ASRU ICASSP BioMS IPTA IJCB ICPR ICASSP CVPR Workshops ICCST Handbook of Biometric Anti-Spoofing, 2nd Ed. IJCB EUSIPCO Media Watermarking, Security, and Forensics AAMAS AAMAS WIFS ISDCS
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