Adversarial Examples Are Not Easily Detected: Bypassing Ten Detection Methods

20 May 2017

Papers citing "Adversarial Examples Are Not Easily Detected: Bypassing Ten Detection Methods"

50 / 352 papers shown

Title
Dompteur: Taming Audio Adversarial Examples Thorsten Eisenhofer Lea Schonherr Joel Frank Lars Speckemeier D. Kolossa Thorsten Holz AAML 36 24 0 10 Feb 2021
"What's in the box?!": Deflecting Adversarial Attacks by Randomly Deploying Adversarially-Disjoint Models Sahar Abdelnabi Mario Fritz AAML 27 7 0 09 Feb 2021
Adversarial Attacks and Defenses in Physiological Computing: A Systematic Review Dongrui Wu Jiaxin Xu Weili Fang Yi Zhang Liuqing Yang Xiaodong Xu Hanbin Luo Xiang Yu AAML 27 25 0 04 Feb 2021
Increasing the Confidence of Deep Neural Networks by Coverage Analysis Giulio Rossolini Alessandro Biondi Giorgio Buttazzo AAML 26 13 0 28 Jan 2021
Error Diffusion Halftoning Against Adversarial Examples Shao-Yuan Lo Vishal M. Patel DiffM 10 4 0 23 Jan 2021
Fundamental Tradeoffs in Distributionally Adversarial Training M. Mehrabi Adel Javanmard Ryan A. Rossi Anup B. Rao Tung Mai AAML 20 17 0 15 Jan 2021
The Vulnerability of Semantic Segmentation Networks to Adversarial Attacks in Autonomous Driving: Enhancing Extensive Environment Sensing Andreas Bär Jonas Löhdefink Nikhil Kapoor Serin Varghese Fabian Hüger Peter Schlicht Tim Fingscheidt AAML 108 33 0 11 Jan 2021
Demystifying Deep Neural Networks Through Interpretation: A Survey Giang Dao Minwoo Lee FaML FAtt 22 1 0 13 Dec 2020
Omni: Automated Ensemble with Unexpected Models against Adversarial Evasion Attack Rui Shu Tianpei Xia Laurie A. Williams Tim Menzies AAML 32 15 0 23 Nov 2020
Adversarial Classification: Necessary conditions and geometric flows Nicolas García Trillos Ryan W. Murray AAML 34 19 0 21 Nov 2020
The Vulnerability of the Neural Networks Against Adversarial Examples in Deep Learning Algorithms Rui Zhao AAML 34 1 0 02 Nov 2020
GreedyFool: Distortion-Aware Sparse Adversarial Attack Xiaoyi Dong Dongdong Chen Jianmin Bao Chuan Qin Lu Yuan Weiming Zhang Nenghai Yu Dong Chen AAML 18 63 0 26 Oct 2020
Optimism in the Face of Adversity: Understanding and Improving Deep Learning through Adversarial Robustness Guillermo Ortiz-Jiménez Apostolos Modas Seyed-Mohsen Moosavi-Dezfooli P. Frossard AAML 29 48 0 19 Oct 2020
Uncovering the Limits of Adversarial Training against Norm-Bounded Adversarial Examples Sven Gowal Chongli Qin J. Uesato Timothy A. Mann Pushmeet Kohli AAML 17 324 0 07 Oct 2020
Query complexity of adversarial attacks Grzegorz Gluch R. Urbanke AAML 19 5 0 02 Oct 2020
An Empirical Study of DNNs Robustification Inefficacy in Protecting Visual Recommenders Vito Walter Anelli Tommaso Di Noia Daniele Malitesta Felice Antonio Merra AAML 24 2 0 02 Oct 2020
Block-wise Image Transformation with Secret Key for Adversarially Robust Defense Maungmaung Aprilpyone Hitoshi Kiya 29 57 0 02 Oct 2020
Optimal Provable Robustness of Quantum Classification via Quantum Hypothesis Testing Maurice Weber Nana Liu Bo-wen Li Ce Zhang Zhikuan Zhao AAML 37 28 0 21 Sep 2020
Certifying Confidence via Randomized Smoothing Aounon Kumar Alexander Levine S. Feizi Tom Goldstein UQCV 33 39 0 17 Sep 2020
Adversarial Machine Learning in Image Classification: A Survey Towards the Defender's Perspective G. R. Machado Eugênio Silva R. Goldschmidt AAML 33 156 0 08 Sep 2020
Witches' Brew: Industrial Scale Data Poisoning via Gradient Matching Jonas Geiping Liam H. Fowl Yifan Jiang W. Czaja Gavin Taylor Michael Moeller Tom Goldstein AAML 19 215 0 04 Sep 2020
Ramifications of Approximate Posterior Inference for Bayesian Deep Learning in Adversarial and Out-of-Distribution Settings John Mitros A. Pakrashi Brian Mac Namee UQCV 26 2 0 03 Sep 2020
PermuteAttack: Counterfactual Explanation of Machine Learning Credit Scorecards Masoud Hashemi Ali Fathi AAML 10 32 0 24 Aug 2020
Defending Adversarial Examples via DNN Bottleneck Reinforcement Wenqing Liu Miaojing Shi Teddy Furon Li Li AAML 21 8 0 12 Aug 2020
Optimizing Information Loss Towards Robust Neural Networks Philip Sperl Konstantin Böttinger AAML 18 3 0 07 Aug 2020
Adversarial Examples on Object Recognition: A Comprehensive Survey A. Serban E. Poll Joost Visser AAML 27 73 0 07 Aug 2020
Robust Machine Learning via Privacy/Rate-Distortion Theory Ye Wang Shuchin Aeron Adnan Siraj Rakin T. Koike-Akino P. Moulin OOD 22 6 0 22 Jul 2020
Towards Visual Distortion in Black-Box Attacks Nannan Li Zhenzhong Chen 25 12 0 21 Jul 2020
Robust Image Classification Using A Low-Pass Activation Function and DCT Augmentation Md Tahmid Hossain S. Teng Ferdous Sohel Guojun Lu 16 10 0 18 Jul 2020
Do Adversarially Robust ImageNet Models Transfer Better? Hadi Salman Andrew Ilyas Logan Engstrom Ashish Kapoor A. Madry 37 417 0 16 Jul 2020
Adversarial Attacks against Neural Networks in Audio Domain: Exploiting Principal Components Ken Alparslan Yigit Can Alparslan Matthew Burlick AAML 21 8 0 14 Jul 2020
Beyond Perturbations: Learning Guarantees with Arbitrary Adversarial Test Examples S. Goldwasser Adam Tauman Kalai Y. Kalai Omar Montasser AAML 19 38 0 10 Jul 2020
How benign is benign overfitting? Amartya Sanyal P. Dokania Varun Kanade Philip Torr NoLa AAML 23 57 0 08 Jul 2020
Deep Learning Defenses Against Adversarial Examples for Dynamic Risk Assessment Xabier Echeberria-Barrio Amaia Gil-Lerchundi Ines Goicoechea-Telleria Raul Orduna Urrutia AAML 11 5 0 02 Jul 2020
Improving Calibration through the Relationship with Adversarial Robustness Yao Qin Xuezhi Wang Alex Beutel Ed H. Chi AAML 40 25 0 29 Jun 2020
Beware the Black-Box: on the Robustness of Recent Defenses to Adversarial Examples Kaleel Mahmood Deniz Gurevin Marten van Dijk Phuong Ha Nguyen AAML 14 22 0 18 Jun 2020
Tricking Adversarial Attacks To Fail Blerta Lindqvist AAML 8 0 0 08 Jun 2020
Adversarial Item Promotion: Vulnerabilities at the Core of Top-N Recommenders that Use Images to Address Cold Start Zhuoran Liu Martha Larson DiffM 20 27 0 02 Jun 2020
Adversarial Classification via Distributional Robustness with Wasserstein Ambiguity Nam Ho-Nguyen Stephen J. Wright OOD 50 16 0 28 May 2020
Vulnerability of deep neural networks for detecting COVID-19 cases from chest X-ray images to universal adversarial attacks Hokuto Hirano K. Koga Kazuhiro Takemoto AAML 21 47 0 22 May 2020
PatchGuard: A Provably Robust Defense against Adversarial Patches via Small Receptive Fields and Masking Chong Xiang A. Bhagoji Vikash Sehwag Prateek Mittal AAML 30 29 0 17 May 2020
Enhancing Intrinsic Adversarial Robustness via Feature Pyramid Decoder Guanlin Li Shuya Ding Jun Luo Chang-rui Liu AAML 50 19 0 06 May 2020
Adversarial Training against Location-Optimized Adversarial Patches Sukrut Rao David Stutz Bernt Schiele AAML 19 91 0 05 May 2020
Towards Characterizing Adversarial Defects of Deep Learning Software from the Lens of Uncertainty Xiyue Zhang Xiaofei Xie Lei Ma Xiaoning Du Q. Hu Yang Liu Jianjun Zhao Meng Sun AAML 16 76 0 24 Apr 2020
Advanced Evasion Attacks and Mitigations on Practical ML-Based Phishing Website Classifiers Yusi Lei Sen Chen Lingling Fan Fu Song Yang Liu AAML 33 50 0 15 Apr 2020
Certifiable Robustness to Adversarial State Uncertainty in Deep Reinforcement Learning Michael Everett Bjorn Lutjens Jonathan P. How AAML 13 41 0 11 Apr 2020
DaST: Data-free Substitute Training for Adversarial Attacks Mingyi Zhou Jing Wu Yipeng Liu Shuaicheng Liu Ce Zhu 22 142 0 28 Mar 2020
Systematic Evaluation of Privacy Risks of Machine Learning Models Liwei Song Prateek Mittal MIACV 196 359 0 24 Mar 2020
Adversarial Robustness on In- and Out-Distribution Improves Explainability Maximilian Augustin Alexander Meinke Matthias Hein OOD 75 98 0 20 Mar 2020
Breaking certified defenses: Semantic adversarial examples with spoofed robustness certificates Amin Ghiasi Ali Shafahi Tom Goldstein 33 55 0 19 Mar 2020