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• W4378977274 abstract "Recent years have seen a proliferation of research on adversarial machine learning. Numerous papers demonstrate powerful algorithmic attacks against a wide variety of machine learning (ML) models, and numerous other papers propose defenses that can withstand most attacks. However, abundant real-world evidence suggests that actual attackers use simple tactics to subvert ML-driven systems, and as a result security practitioners have not prioritized adversarial ML defenses. Motivated by the apparent gap between researchers and practitioners, this position paper aims to bridge the two domains. We first present three real-world case studies from which we can glean practical insights unknown or neglected in research. Next we analyze all adversarial ML papers recently published in top security conferences, highlighting positive trends and blind spots. Finally, we state positions on precise and cost-driven threat modeling, collaboration between industry and academia, and reproducible research. We believe that our positions, if adopted, will increase the real-world impact of future endeavours in adver-sarial ML, bringing both researchers and practitioners closer to their shared goal of improving the security of ML systems." @default.
• W4378977274 created "2023-06-02" @default.
• W4378977274 creator A5005817824 @default.
• W4378977274 creator A5015959942 @default.
• W4378977274 creator A5052168623 @default.
• W4378977274 creator A5063585200 @default.
• W4378977274 creator A5072597369 @default.
• W4378977274 creator A5084036967 @default.
• W4378977274 date "2023-02-01" @default.
• W4378977274 modified "2023-10-01" @default.
• W4378977274 title "“Real Attackers Don't Compute Gradients”: Bridging the Gap Between Adversarial ML Research and Practice" @default.
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