Author: Chong Xiang
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Toward Trustworthy Machine Learning: An Example in Defending against Adversarial Patch Attacks (2)
By Chong Xiang and Prateek Mittal In our previous post, we discussed adversarial patch attacks and presented our first defense algorithm PatchGuard. The PatchGuard framework (small receptive field + secure aggregation) has become the most popular defense strategy over the past year, subsuming a long list of defense instances (Clipped BagNet, De-randomized Smoothing, BagCert, Randomized…
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Toward Trustworthy Machine Learning: An Example in Defending against Adversarial Patch Attacks
By Chong Xiang and Prateek Mittal Thanks to the stunning advancement of Machine Learning (ML) technologies, ML models are increasingly being used in critical societal contexts — such as in the courtroom, where judges look to ML models to determine whether a defendant is a flight risk, and in autonomous driving, where driverless vehicles are…