Adversarial Machine Learning: Attacks, Defenses, and Open Challenges
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Abstract (arXiv)
Adversarial Machine Learning (AML) addresses vulnerabilities in AI systems where adversaries manipulate inputs or training data to degrade performance. This article provides a comprehensive analysis of evasion and poisoning attacks, formalizes defense mechanisms with mathematical rigor, and discusses the challenges of implementing robust solutions in adaptive threat models. Additionally, it highlights open challenges in certified robustness, scalability, and real-world deployment.
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References · 12
- 2014ImageNet Large Scale Visual Recognition Challengecited by 8,498
- 2014Explaining and Harnessing Adversarial Examplescited by 7,364
- 2017Towards Deep Learning Models Resistant to Adversarial Attackscited by 6,415
- 2016Towards Evaluating the Robustness of Neural Networkscited by 2,998
- 2016Deep Learning with Differential Privacycited by 2,728
- 2017Understanding Black-box Predictions via Influence Functionscited by 1,741
- 2019Certified Adversarial Robustness via Randomized Smoothingcited by 1,340
- 2018Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examplescited by 1,268
- 2018Improving Transferability of Adversarial Examples with Input Diversitycited by 599
- 2017Countering Adversarial Images using Input Transformationscited by 520
- 2018Poison Frogs! Targeted Clean-Label Poisoning Attacks on Neural Networkscited by 519
- 2018On the Effectiveness of Interval Bound Propagation for Training Verifiably Robust Modelscited by 328
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