Transferability in Machine Learning: from Phenomena to Black-Box Attacks using Adversarial Samples

24 May 2016

Papers citing "Transferability in Machine Learning: from Phenomena to Black-Box Attacks using Adversarial Samples"

50 / 360 papers shown

Title
Minimizing Maximum Model Discrepancy for Transferable Black-box Targeted Attacks Anqi Zhao Tong Chu Yahao Liu Wen Li Jingjing Li Lixin Duan AAML 28 16 0 18 Dec 2022
A Survey on Reinforcement Learning Security with Application to Autonomous Driving Ambra Demontis Maura Pintor Christian Scano Kathrin Grosse Hsiao-Ying Lin Chengfang Fang Battista Biggio Fabio Roli AAML 49 4 0 12 Dec 2022
Carpet-bombing patch: attacking a deep network without usual requirements Pol Labarbarie Adrien Chan-Hon-Tong Stéphane Herbin Milad Leyli-Abadi AAML 32 1 0 12 Dec 2022
General Adversarial Defense Against Black-box Attacks via Pixel Level and Feature Level Distribution Alignments Xiaogang Xu Hengshuang Zhao Philip Torr Jiaya Jia AAML 37 2 0 11 Dec 2022
Leveraging Unlabeled Data to Track Memorization Mahsa Forouzesh Hanie Sedghi Patrick Thiran NoLa TDI 39 4 0 08 Dec 2022
Enhancing Quantum Adversarial Robustness by Randomized Encodings Weiyuan Gong D. Yuan Weikang Li D. Deng AAML 26 19 0 05 Dec 2022
Interpretations Cannot Be Trusted: Stealthy and Effective Adversarial Perturbations against Interpretable Deep Learning Eldor Abdukhamidov Mohammed Abuhamad Simon S. Woo Eric Chan-Tin Tamer Abuhmed AAML 36 9 0 29 Nov 2022
Game Theoretic Mixed Experts for Combinational Adversarial Machine Learning Ethan Rathbun Kaleel Mahmood Sohaib Ahmad Caiwen Ding Marten van Dijk AAML 24 4 0 26 Nov 2022
Adversarial Attacks are a Surprisingly Strong Baseline for Poisoning Few-Shot Meta-Learners E. T. Oldewage J. Bronskill Richard Turner 27 3 0 23 Nov 2022
Understanding the Vulnerability of Skeleton-based Human Activity Recognition via Black-box Attack Yunfeng Diao He Wang Tianjia Shao Yong-Liang Yang Kun Zhou David C. Hogg Meng Wang AAML 45 7 0 21 Nov 2022
Butterfly Effect Attack: Tiny and Seemingly Unrelated Perturbations for Object Detection N. Doan Arda Yüksel Chih-Hong Cheng AAML 21 1 0 14 Nov 2022
What Can the Neural Tangent Kernel Tell Us About Adversarial Robustness? Nikolaos Tsilivis Julia Kempe AAML 52 18 0 11 Oct 2022
On the Robustness of Deep Clustering Models: Adversarial Attacks and Defenses Anshuman Chhabra Ashwin Sekhari P. Mohapatra OOD AAML 45 8 0 04 Oct 2022
Watch What You Pretrain For: Targeted, Transferable Adversarial Examples on Self-Supervised Speech Recognition models R. Olivier H. Abdullah Bhiksha Raj AAML 26 1 0 17 Sep 2022
On the Transferability of Adversarial Examples between Encrypted Models Miki Tanaka Isao Echizen Hitoshi Kiya SILM 39 4 0 07 Sep 2022
Adversarial Detection: Attacking Object Detection in Real Time Han-Ching Wu Syed Yunas Sareh Rowlands Wenjie Ruan Johan Wahlstrom AAML 33 4 0 05 Sep 2022
Trace and Detect Adversarial Attacks on CNNs using Feature Response Maps Mohammadreza Amirian Friedhelm Schwenker Thilo Stadelmann AAML 27 16 0 24 Aug 2022
Success of Uncertainty-Aware Deep Models Depends on Data Manifold Geometry M. Penrod Harrison Termotto Varshini Reddy Jiayu Yao Finale Doshi-Velez Weiwei Pan AAML OOD 43 1 0 02 Aug 2022
LGV: Boosting Adversarial Example Transferability from Large Geometric Vicinity Martin Gubri Maxime Cordy Mike Papadakis Yves Le Traon Koushik Sen AAML 35 51 0 26 Jul 2022
Decorrelative Network Architecture for Robust Electrocardiogram Classification Christopher Wiedeman Ge Wang OOD 13 2 0 19 Jul 2022
Adversarial Pixel Restoration as a Pretext Task for Transferable Perturbations H. Malik Shahina Kunhimon Muzammal Naseer Salman Khan Fahad Shahbaz Khan AAML 33 8 0 18 Jul 2022
Watermark Vaccine: Adversarial Attacks to Prevent Watermark Removal Xinwei Liu Jian Liu Yang Bai Jindong Gu Tao Chen Xiaojun Jia Xiaochun Cao AAML WIGM 33 26 0 17 Jul 2022
Provably Adversarially Robust Nearest Prototype Classifiers Václav Voráček Matthias Hein AAML 25 11 0 14 Jul 2022
Machine Learning Security in Industry: A Quantitative Survey Kathrin Grosse L. Bieringer Tarek R. Besold Battista Biggio Katharina Krombholz 40 32 0 11 Jul 2022
Statistical Detection of Adversarial examples in Blockchain-based Federated Forest In-vehicle Network Intrusion Detection Systems I. Aliyu Sélinde Van Engelenburg Muhammed Muazu Jinsul Kim C. Lim AAML 43 14 0 11 Jul 2022
Threat Assessment in Machine Learning based Systems L. Tidjon Foutse Khomh 27 17 0 30 Jun 2022
Transferable Graph Backdoor Attack Shuiqiao Yang Bao Gia Doan Paul Montague O. Vel Tamas Abraham S. Çamtepe Damith C. Ranasinghe S. Kanhere AAML 49 36 0 21 Jun 2022
I Know What You Trained Last Summer: A Survey on Stealing Machine Learning Models and Defences Daryna Oliynyk Rudolf Mayer Andreas Rauber 57 106 0 16 Jun 2022
ReFace: Real-time Adversarial Attacks on Face Recognition Systems Shehzeen Samarah Hussain Todd P. Huster Chris Mesterharm Paarth Neekhara Kevin R. An Malhar Jere Harshvardhan Digvijay Sikka F. Koushanfar AAML 26 6 0 09 Jun 2022
Pruning has a disparate impact on model accuracy Cuong Tran Ferdinando Fioretto Jung-Eun Kim Rakshit Naidu 45 38 0 26 May 2022
An Analytic Framework for Robust Training of Artificial Neural Networks R. Barati Reza Safabakhsh Mohammad Rahmati AAML 19 0 0 26 May 2022
Transferable Adversarial Attack based on Integrated Gradients Y. Huang A. Kong AAML 40 50 0 26 May 2022
Learn2Weight: Parameter Adaptation against Similar-domain Adversarial Attacks Siddhartha Datta AAML 38 4 0 15 May 2022
Do You Think You Can Hold Me? The Real Challenge of Problem-Space Evasion Attacks Harel Berger A. Dvir Chen Hajaj Rony Ronen AAML 29 3 0 09 May 2022
A Simple Yet Efficient Method for Adversarial Word-Substitute Attack Tianle Li Yi Yang AAML 32 0 0 07 May 2022
Detecting Backdoor Poisoning Attacks on Deep Neural Networks by Heatmap Clustering Lukas Schulth Christian Berghoff Matthias Neu AAML 25 5 0 27 Apr 2022
A Survey of Robust Adversarial Training in Pattern Recognition: Fundamental, Theory, and Methodologies Zhuang Qian Kaizhu Huang Qiufeng Wang Xu-Yao Zhang OOD AAML ObjD 54 72 0 26 Mar 2022
Improving the Transferability of Targeted Adversarial Examples through Object-Based Diverse Input Junyoung Byun Seungju Cho Myung-Joon Kwon Heeseon Kim Changick Kim AAML DiffM 29 68 0 17 Mar 2022
One Parameter Defense -- Defending against Data Inference Attacks via Differential Privacy Dayong Ye Sheng Shen Tianqing Zhu B. Liu Wanlei Zhou MIACV 16 62 0 13 Mar 2022
Practical No-box Adversarial Attacks with Training-free Hybrid Image Transformation Qilong Zhang Chaoning Zhang Chaoning Zhang Chaoqun Li Xuanhan Wang Jingkuan Song Lianli Gao AAML 41 21 0 09 Mar 2022
Art-Attack: Black-Box Adversarial Attack via Evolutionary Art P. Williams Ke Li AAML 27 2 0 07 Mar 2022
Adversarial robustness of sparse local Lipschitz predictors Ramchandran Muthukumar Jeremias Sulam AAML 34 13 0 26 Feb 2022
StratDef: Strategic Defense Against Adversarial Attacks in ML-based Malware Detection Aqib Rashid Jose Such AAML 24 6 0 15 Feb 2022
Holistic Adversarial Robustness of Deep Learning Models Pin-Yu Chen Sijia Liu AAML 54 16 0 15 Feb 2022
Adversarial Attacks and Defense Methods for Power Quality Recognition Jiwei Tian Buhong Wang Jing Li Zhen Wang Mete Ozay AAML 28 0 0 11 Feb 2022
Red Teaming Language Models with Language Models Ethan Perez Saffron Huang Francis Song Trevor Cai Roman Ring John Aslanides Amelia Glaese Nat McAleese G. Irving AAML 13 613 0 07 Feb 2022
Beyond ImageNet Attack: Towards Crafting Adversarial Examples for Black-box Domains Qilong Zhang Xiaodan Li YueFeng Chen Jingkuan Song Lianli Gao Yuan He Hui Xue AAML 67 64 0 27 Jan 2022
Security for Machine Learning-based Software Systems: a survey of threats, practices and challenges Huaming Chen Muhammad Ali Babar AAML 42 22 0 12 Jan 2022
On Distinctive Properties of Universal Perturbations Sung Min Park K. Wei Kai Y. Xiao Jungshian Li A. Madry AAML 30 2 0 31 Dec 2021
Closer Look at the Transferability of Adversarial Examples: How They Fool Different Models Differently Futa Waseda Sosuke Nishikawa Trung-Nghia Le H. Nguyen Isao Echizen SILM 36 35 0 29 Dec 2021