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Wild Patterns: Ten Years After the Rise of Adversarial Machine Learning

8 December 2017

Battista Biggio

Fabio Roli

AAML

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Papers citing "Wild Patterns: Ten Years After the Rise of Adversarial Machine Learning"

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Title
The Feasibility and Inevitability of Stealth Attacks I. Tyukin D. Higham Alexander Bastounis Eliyas Woldegeorgis Alexander N. Gorban AAML 48 19 0 26 Jun 2021
Estimating the Robustness of Classification Models by the Structure of the Learned Feature-Space Kalun Ho Franz-Josef Pfreundt J. Keuper Margret Keuper OOD UQCV 50 3 0 23 Jun 2021
Adversarial Training Helps Transfer Learning via Better Representations Zhun Deng Linjun Zhang Kailas Vodrahalli Kenji Kawaguchi James Zou GAN 89 54 0 18 Jun 2021
Indicators of Attack Failure: Debugging and Improving Optimization of Adversarial Examples Maura Pintor Christian Scano Angelo Sotgiu Ambra Demontis Nicholas Carlini Battista Biggio Fabio Roli AAML 88 28 0 18 Jun 2021
Bad Characters: Imperceptible NLP Attacks Nicholas Boucher Ilia Shumailov Ross J. Anderson Nicolas Papernot AAML SILM 102 107 0 18 Jun 2021
Modeling Realistic Adversarial Attacks against Network Intrusion Detection Systems Giovanni Apruzzese M. Andreolini Luca Ferretti Mirco Marchetti M. Colajanni AAML 102 109 0 17 Jun 2021
Model Extraction and Adversarial Attacks on Neural Networks using Switching Power Information Tommy Li Cory E. Merkel AAML 31 5 0 15 Jun 2021
Certification of embedded systems based on Machine Learning: A survey Guillaume Vidot Christophe Gabreau I. Ober Iulian Ober 51 12 0 14 Jun 2021
AdaMatch: A Unified Approach to Semi-Supervised Learning and Domain Adaptation David Berthelot Rebecca Roelofs Kihyuk Sohn Nicholas Carlini Alexey Kurakin 61 145 0 08 Jun 2021
Markpainting: Adversarial Machine Learning meets Inpainting David Khachaturov Ilia Shumailov Yiren Zhao Nicolas Papernot Ross J. Anderson WIGM 65 14 0 01 Jun 2021
Gradient-based Data Subversion Attack Against Binary Classifiers Rosni Vasu Sanjay Seetharaman Shubham Malaviya Manish Shukla S. Lodha AAML 31 0 0 31 May 2021
A BIC-based Mixture Model Defense against Data Poisoning Attacks on Classifiers Xi Li David J. Miller Zhen Xiang G. Kesidis AAML 38 0 0 28 May 2021
On the Robustness of Domain Constraints Ryan Sheatsley Blaine Hoak Eric Pauley Yohan Beugin Mike Weisman Patrick McDaniel AAML OOD 88 26 0 18 May 2021
Lightweight Distributed Gaussian Process Regression for Online Machine Learning Zhenyuan Yuan Minghui Zhu 50 4 0 11 May 2021
De-Pois: An Attack-Agnostic Defense against Data Poisoning Attacks Jian Chen Xuxin Zhang Rui Zhang Chen Wang Ling Liu AAML 72 88 0 08 May 2021
Topological Uncertainty: Monitoring trained neural networks through persistence of activation graphs Théo Lacombe Yuichi Ike Mathieu Carrière Frédéric Chazal Marc Glisse Yuhei Umeda 75 23 0 07 May 2021
On the Adversarial Robustness of Quantized Neural Networks Micah Gorsline James T. Smith Cory E. Merkel AAML 85 19 0 01 May 2021
Adversarial Example Detection for DNN Models: A Review and Experimental Comparison Ahmed Aldahdooh W. Hamidouche Sid Ahmed Fezza Olivier Déforges AAML 233 128 0 01 May 2021
IPatch: A Remote Adversarial Patch Yisroel Mirsky AAML 62 12 0 30 Apr 2021
Influence Based Defense Against Data Poisoning Attacks in Online Learning Sanjay Seetharaman Shubham Malaviya KV Rosni Manish Shukla S. Lodha TDI AAML 111 9 0 24 Apr 2021
Turning Federated Learning Systems Into Covert Channels Gabriele Costa Fabio Pinelli S. Soderi Gabriele Tolomei FedML 70 12 0 21 Apr 2021
Prospective Artificial Intelligence Approaches for Active Cyber Defence Neil Dhir H. Hoeltgebaum N. Adams M. Briers A. Burke Paul Jones AAML 80 28 0 20 Apr 2021
Provable Robustness of Adversarial Training for Learning Halfspaces with Noise Difan Zou Spencer Frei Quanquan Gu 58 13 0 19 Apr 2021
Robust Learning Meets Generative Models: Can Proxy Distributions Improve Adversarial Robustness? Vikash Sehwag Saeed Mahloujifar Tinashe Handina Sihui Dai Chong Xiang M. Chiang Prateek Mittal OOD 106 131 0 19 Apr 2021
Lower Bounds on Cross-Entropy Loss in the Presence of Test-time Adversaries A. Bhagoji Daniel Cullina Vikash Sehwag Prateek Mittal AAML OOD 73 3 0 16 Apr 2021
Random and Adversarial Bit Error Robustness: Energy-Efficient and Secure DNN Accelerators David Stutz Nandhini Chandramoorthy Matthias Hein Bernt Schiele AAML MQ 68 18 0 16 Apr 2021
Evaluating Standard Feature Sets Towards Increased Generalisability and Explainability of ML-based Network Intrusion Detection Mohanad Sarhan S. Layeghy Marius Portmann 67 69 0 15 Apr 2021
A Backdoor Attack against 3D Point Cloud Classifiers Zhen Xiang David J. Miller Siheng Chen Xi Li G. Kesidis 3DPC AAML 84 77 0 12 Apr 2021
Relating Adversarially Robust Generalization to Flat Minima David Stutz Matthias Hein Bernt Schiele OOD 105 67 0 09 Apr 2021
Adversarial Robustness Guarantees for Gaussian Processes A. Patané Arno Blaas Luca Laurenti L. Cardelli Stephen J. Roberts Marta Z. Kwiatkowska GP AAML 188 9 0 07 Apr 2021
Achieving Transparency Report Privacy in Linear Time Chien-Lun Chen L. Golubchik R. Pal 73 4 0 31 Mar 2021
A Variational Inequality Approach to Bayesian Regression Games Wenshuo Guo Michael I. Jordan Tianyi Lin 40 5 0 24 Mar 2021
Black-box Detection of Backdoor Attacks with Limited Information and Data Yinpeng Dong Xiao Yang Zhijie Deng Tianyu Pang Zihao Xiao Hang Su Jun Zhu AAML 91 114 0 24 Mar 2021
The Hammer and the Nut: Is Bilevel Optimization Really Needed to Poison Linear Classifiers? Antonio Emanuele Cinà Sebastiano Vascon Ambra Demontis Battista Biggio Fabio Roli Marcello Pelillo AAML 66 10 0 23 Mar 2021
Explainable Adversarial Attacks in Deep Neural Networks Using Activation Profiles G. Cantareira R. Mello F. Paulovich AAML 57 9 0 18 Mar 2021
ReinforceBug: A Framework to Generate Adversarial Textual Examples Bushra Sabir M. Babar R. Gaire SILM AAML 57 3 0 11 Mar 2021
Proof-of-Learning: Definitions and Practice Hengrui Jia Mohammad Yaghini Christopher A. Choquette-Choo Natalie Dullerud Anvith Thudi Varun Chandrasekaran Nicolas Papernot AAML 84 106 0 09 Mar 2021
Pseudo-labeling for Scalable 3D Object Detection Benjamin Caine Rebecca Roelofs Vijay Vasudevan Jiquan Ngiam Yuning Chai Zhiwen Chen Jonathon Shlens 87 42 0 02 Mar 2021
Adversarial Examples can be Effective Data Augmentation for Unsupervised Machine Learning Chia-Yi Hsu Pin-Yu Chen Songtao Lu Sijia Liu Chia-Mu Yu AAML 93 11 0 02 Mar 2021
Fast Minimum-norm Adversarial Attacks through Adaptive Norm Constraints Maura Pintor Fabio Roli Wieland Brendel Battista Biggio AAML 92 73 0 25 Feb 2021
Non-Singular Adversarial Robustness of Neural Networks Yu-Lin Tsai Chia-Yi Hsu Chia-Mu Yu Pin-Yu Chen AAML OOD 56 5 0 23 Feb 2021
Model-Based Domain Generalization Alexander Robey George J. Pappas Hamed Hassani OOD 151 131 0 23 Feb 2021
Universal Adversarial Examples and Perturbations for Quantum Classifiers Weiyuan Gong D. Deng AAML 88 25 0 15 Feb 2021
Resilient Machine Learning for Networked Cyber Physical Systems: A Survey for Machine Learning Security to Securing Machine Learning for CPS Felix O. Olowononi D. Rawat Chunmei Liu 95 138 0 14 Feb 2021
Realizable Universal Adversarial Perturbations for Malware Raphael Labaca-Castro Luis Muñoz-González Feargus Pendlebury Gabi Dreo Rodosek Fabio Pierazzi Lorenzo Cavallaro AAML 63 6 0 12 Feb 2021
Fairness-Aware PAC Learning from Corrupted Data Nikola Konstantinov Christoph H. Lampert 84 19 0 11 Feb 2021
Defense Against Reward Poisoning Attacks in Reinforcement Learning Kiarash Banihashem Adish Singla Goran Radanović AAML 92 27 0 10 Feb 2021
Bayesian Inference with Certifiable Adversarial Robustness Matthew Wicker Luca Laurenti A. Patané Zhoutong Chen Zheng Zhang Marta Z. Kwiatkowska AAML BDL 126 30 0 10 Feb 2021
Better Safe Than Sorry: Preventing Delusive Adversaries with Adversarial Training Lue Tao Lei Feng Jinfeng Yi Sheng-Jun Huang Songcan Chen AAML 143 73 0 09 Feb 2021
Security and Privacy for Artificial Intelligence: Opportunities and Challenges Ayodeji Oseni Nour Moustafa Helge Janicke Peng Liu Z. Tari A. Vasilakos AAML 67 52 0 09 Feb 2021