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Effective and Robust detection of adversarial Examples via Benford-Fourier Coefficients

Machine Intelligence Research 2023年第5期20卷 666-682页

作者： Cheng-Cheng Ma Bao-Yuan Wu Yan-Bo Fan Yong Zhang Zhi-Feng Li National Laboratory of Pattern Recognition Institute of AutomationChinese Academy of SciencesBeijing 100190China School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049China School of Data Science The Chinese University of Hong KongShenzhen 518172China Shenzhen Research Institute of Big Data Shenzhen 518172China AI Lab Tencent Inc.Shenzhen 518057China

adversarial example has been well known as a serious threat to deep neural networks(DNNs).In this work,we study the detection of adversarial examples based on the assumption that the output and internal responses of one DNN model for both adversarial and benign examples follow the generalized Gaussian distribution(GGD)but with different parameters(i.e.,shape factor,mean,and variance).GGD is a general distribution family that covers many popular distributions(e.g.,Laplacian,Gaussian,or uniform).Therefore,it is more likely to approximate the intrinsic distributions of internal responses than any specific ***,since the shape factor is more robust to different databases rather than the other two parameters,we propose to construct discriminative features via the shape factor for adversarial detection,employing the magnitude of Benford-Fourier(MBF)coefficients,which can be easily estimated using ***,a support vector machine is trained as an adversarial detector leveraging the MBF *** experiments in terms of image classification demonstrate that the proposed detector is much more effective and robust in detecting adversarial examples of different crafting methods and sources compared to state-of-the-art adversarial detection methods.

关键词： adversarial defense adversarial detection generalized Gaussian distribution Benford-Fourier coefficients image classification

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An end-to-end convolutional network for joint detecting and denoising adversarial perturbations in vehicle classification

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Computational Visual Media 2021年第2期7卷 217-227页

作者： Peng Liu Huiyuan Fu Huadong Ma Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia Beijing University of Posts and TelecommunicationsBeijing 100876China

Deep convolutional neural networks(DCNNs)have been widely deployed in real-world ***,DCNNs are easily tricked by adversarial examples,which present challenges for critical applications,such as vehicle *** address this problem,we propose a novel end-to-end convolutional network for joint detection and removal of adversarial perturbations by denoising(DDAP).It gets rid of adversarial perturbations using the DDAP denoiser based on adversarial examples discovered by the DDAP *** proposed method can be regarded as a pre-processing step—it does not require modifying the structure of the vehicle classification model and hardly affects the classification results on clean *** consider four kinds of adversarial attack(FGSM,BIM,DeepFool,PGD)to verify DDAP’s capabilities when trained on BIT-Vehicle and other public *** provides better defense than other state-of-the-art defensive methods.

关键词： adversarial defense adversarial detection vehicle classification deep learning

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VeriFace:Defending against adversarial Attacks in Face Verification Systems

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Computers, Materials & Continua 2023年第9期76卷 3151-3166页

作者： Awny Sayed Sohair Kinlany Alaa Zaki Ahmed Mahfouz Information Technology Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddahSaudi Arabia Computer Science Department Faculty of ScienceMinia UniversityAl MinyaEgypt Faculty of Computer Studies Arab Open UniversityMuscatOman

Face verification systems are critical in a wide range of applications,such as security systems and biometric ***,these systems are vulnerable to adversarial attacks,which can significantly compromise their accuracy and *** attacks are designed to deceive the face verification system by adding subtle perturbations to the input *** perturbations can be imperceptible to the human eye but can cause the systemtomisclassifyor fail torecognize thepersoninthe *** this issue,weproposeanovel system called VeriFace that comprises two defense mechanisms,adversarial detection,and adversarial *** first mechanism,adversarial detection,is designed to identify whether an input image has been subjected to adversarial *** second mechanism,adversarial removal,is designed to remove these perturbations from the input image to ensure the face verification system can accurately recognize the person in the *** evaluate the effectiveness of the VeriFace system,we conducted experiments on different types of adversarial attacks using the Labelled Faces in the Wild(LFW)*** results show that the VeriFace adversarial detector can accurately identify adversarial imageswith a high detection accuracy of 100%.Additionally,our proposedVeriFace adversarial removalmethod has a significantly lower attack success rate of 6.5%compared to state-of-the-art removalmethods.

关键词： adversarial attacks face aerification adversarial detection perturbation removal

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