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Security of Facial Forensics Models Against Adversarial Attacks [article]

Rong Huang, Fuming Fang, Huy H. Nguyen, Junichi Yamagishi, Isao Echizen
2020 arXiv   pre-print
We investigated several DNN-based forgery forensics models (FFMs) to examine whether they are secure against adversarial attacks.  ...  These findings provide a baseline for evaluating the adversarial security of FFMs.  ...  There has been a lack of work on whether FFMs are secure against adversarial attacks.  ... 
arXiv:1911.00660v2 fatcat:wju34mhrtjegblb2wv3kp4wtie

A Novel Defensive Strategy for Facial Manipulation Detection Combining Bilateral Filtering and Joint Adversarial Training

Yifan Luo, Feng Ye, Bin Weng, Shan Du, Tianqiang Huang, Zhili Zhou
2021 Security and Communication Networks  
The introduction of joint adversarial training can train a model that defends against multiple adversarial attacks.  ...  mitigate the vulnerability of facial manipulation detectors against adversarial examples.  ...  After training, the model can only defend against this kind of adversarial attack method and cannot defend against other kinds of adversarial attack methods.  ... 
doi:10.1155/2021/4280328 fatcat:lijmatyrmfgpdbkz2wfsxh4654

Perception Matters: Exploring Imperceptible and Transferable Anti-forensics for GAN-generated Fake Face Imagery Detection [article]

Yongwei Wang, Xin Ding, Li Ding, Rabab Ward, Z. Jane Wang
2020 arXiv   pre-print
Therefore it makes existing adversarial attacks ineffective as an anti-forensic method.  ...  Here we explore more imperceptible and transferable anti-forensics for fake face imagery detection based on adversarial attacks.  ...  Acknowledgments We acknowledge financial support from the Natural Sciences and Engineering Research Council of Canada (NSERC), and Yongwei Wang acknowledges the China Scholarship Council (CSC) for financial  ... 
arXiv:2010.15886v1 fatcat:emsfw5a5wvfrfgmtls4i5nexfe

Machine Learning Methods for Computer Security (Dagstuhl Perspectives Workshop 12371)

Anthony D. Joseph, Pavel Laskov, Fabio Roli, J. Doug Tygar, Blaine Nelson, Marc Herbstritt
2013 Dagstuhl Reports  
Unlike many other application domains of machine learning, security-related applications require careful consideration of their adversarial nature and novel learning methods with improved robustness against  ...  potential attacks.  ...  privacy exploratory attacks against a learned model [40] .  ... 
doi:10.4230/dagrep.2.9.109 dblp:journals/dagstuhl-reports/JosephLRTN12 fatcat:4x3ng2szxfg5jnkf5rtwsmttrm

Making DeepFakes more spurious: evading deep face forgery detection via trace removal attack [article]

Chi Liu, Huajie Chen, Tianqing Zhu, Jun Zhang, Wanlei Zhou
2022 arXiv   pre-print
To evaluate the attack efficacy, we crafted heterogeneous security scenarios where the detectors were embedded with different levels of defense and the attackers' background knowledge of data varies.  ...  Recently, a few attacks, principally adversarial attacks, have succeeded in cloaking DeepFake images to evade detection.  ...  Anti-forensics for DeepFakes Adversarial attack: Since most forgery detectors are machine learning models, adversarial attacks, as a typical type of attack against machine learning classifiers, have become  ... 
arXiv:2203.11433v1 fatcat:oxidlgwn7rdnxcnt7ab2jl5mzm

Artificial Intelligence Security Threat, Crime, and Forensics: Taxonomy and Open Issues

Doowon Joeng, Doowon Joeng
2020 IEEE Access  
The AI as target crime is a new area of potential criminal activity against AI system; adversarial attack [13] is a typical example.  ...  As it is difficult to defend against adversarial attacks, recent works have attempted to detect AEs instead [129] .  ... 
doi:10.1109/access.2020.3029280 fatcat:ejumqlsfvnhybixww6m4ggtsj4

Law and Adversarial Machine Learning [article]

Ram Shankar Siva Kumar, David R. O'Brien, Kendra Albert, Salome Vilojen
2018 arXiv   pre-print
We end with a call for action to ML researchers to invest in transparent benchmarks of attacks and defenses; architect ML systems with forensics in mind and finally, think more about adversarial machine  ...  Through scenarios grounded in adversarial ML literature, we explore how some aspects of computer crime, copyright, and tort law interface with perturbation, poisoning, model stealing and model inversion  ...  Ram would also like to thank Andi Comissoneru, Sharon Xia, Steve Mott and the entire Azure Security Data Science team for holding the fort during his time away.  ... 
arXiv:1810.10731v3 fatcat:ylgab2xk3zaivgmaixb7afszaa

Robust Deepfake On Unrestricted Media: Generation And Detection [article]

Trung-Nghia Le and Huy H Nguyen and Junichi Yamagishi and Isao Echizen
2022 arXiv   pre-print
It also discusses possible ways to improve the robustness of deepfake detection for a wide variety of media (e.g., in-the-wild images and videos).  ...  This chapter explores the evolution of and challenges in deepfake generation and detection.  ...  Ranking Team Binary Cross-Entropy Loss 1 Forensics 0.2674 2 RealFace 0.3699 3 VISG 0.4060 4 jiashangplus 0.4064 5 Miao 0.4132 Table 7 : 7 [22]ormance of deepfake classifiers against adversarial attacks  ... 
arXiv:2202.06228v1 fatcat:a37q2lf7w5bcbekk5esmbx2goe

FakeSpotter: A Simple yet Robust Baseline for Spotting AI-Synthesized Fake Faces [article]

Run Wang, Felix Juefei-Xu, Lei Ma, Xiaofei Xie, Yihao Huang, Jian Wang, Yang Liu
2020 arXiv   pre-print
They are widely adopted in synthesizing facial images which brings potential security concerns to humans as the fakes spread and fuel the misinformation.  ...  attacks.  ...  We gratefully acknowledge the support of NVIDIA AI Tech Center (NVAITC) to our research.  ... 
arXiv:1909.06122v3 fatcat:mbldjk57lvaw7adsnf5hfb3evu

IEEE Access Special Section: Digital Forensics Through Multimedia Source Inference

Irene Amerini, Chang-Tsun Li, Nasir Memon, Jiwu Huang
2020 IEEE Access  
against crime.  ...  The article ''FD-GAN: Face de-morphing generative adversarial network for restoring accomplice's facial image,'' by Peng et al., studies a face de-morphing generative adversarial network (FD-GAN) to restore  ... 
doi:10.1109/access.2020.3036772 fatcat:digjpvg73zdyhl3xzyo2moc6em

2020 Index IEEE Transactions on Information Forensics and Security Vol. 15

2020 IEEE Transactions on Information Forensics and Security  
., +, TIFS 2020 2514-2527 Fourier transforms A Robust Approach for Securing Audio Classification Against Adversarial Attacks.  ...  ., +, TIFS 2020 1444-1454 Discrete wavelet transforms A Robust Approach for Securing Audio Classification Against Adversarial Attacks.  ... 
doi:10.1109/tifs.2021.3053735 fatcat:eforexmnczeqzdj3sc2j4yoige

Table of contents

2020 IEEE Transactions on Information Forensics and Security  
Headley 1102 A Robust Approach for Securing Audio Classification Against Adversarial Attacks ....................................... ....................................................................  ...  Mishra 3152 Molecular Barcoding as a Defense Against Benchtop Biochemical Attacks on DNA Fingerprinting and Information Forensics .............................................. M. Ibrahim, T.-C.  ... 
doi:10.1109/tifs.2019.2940363 fatcat:hnt75fw6nbduzh2c4x4wxdsey4

Novel Promising Algorithm to suppress Spoof Attack by Cryptography Firewall2014

Prof Hadadi Sudheendra, Dr. N Krishnamurthy
2018 International Journal of Trend in Scientific Research and Development  
Spoof attack suppression by the biometric information incorporation is the new and modern method of and as well suppression the attack online as well off line.  ...  The general public has immense need for security measures against spoof attack. Biometrics is the fastest growing segment of such security industry.  ...  Generalized Attack Detection Model : In public key cryptography the security of private keys is vital importance.  ... 
doi:10.31142/ijtsrd15801 fatcat:p2qz2htdcjc6ndmiifxx3ak7yy

Adversarial Deepfakes: Evaluating Vulnerability of Deepfake Detectors to Adversarial Examples [article]

Paarth Neekhara, Shehzeen Hussain, Malhar Jere, Farinaz Koushanfar and Julian McAuley
2020 arXiv   pre-print
Therefore detection of fake videos has garnered immense interest in academia and industry.  ...  We present pipelines in both white-box and black-box attack scenarios that can fool DNN based Deepfake detectors into classifying fake videos as real.  ...  Particularly, one line of adversarial attack (Athalye et al., 2018a; computes the expected value of gradients for each of the sub-sampled networks/inputs and performs attacks that are robust against  ... 
arXiv:2002.12749v1 fatcat:zydcyz4bajbpljzoray4ssdjpy

SoK: Anti-Facial Recognition Technology [article]

Emily Wenger, Shawn Shan, Haitao Zheng, Ben Y. Zhao
2021 arXiv   pre-print
In response, a broad suite of so-called "anti-facial recognition" (AFR) tools has been developed to help users avoid unwanted facial recognition.  ...  The rapid adoption of facial recognition (FR) technology by both government and commercial entities in recent years has raised concerns about civil liberties and privacy.  ...  Recent leverage the concept ofadversarial perturbations” against works propose the concept of “data leverage” where users DNN models.  ... 
arXiv:2112.04558v1 fatcat:quvw5jffcrfvnh62274axis3ti
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