25 citations · 25 across the 1 of their papers we have counts for
5 papers
PatchGuard++: Efficient Provable Attack Detection against Adversarial Patches
Chong Xiang, Prateek Mittal
An adversarial patch can arbitrarily manipulate image pixels within a restricted region to induce model misclassification. The threat of this localized attack has gained significan…
DetectorGuard: Provably Securing Object Detectors against Localized Patch Hiding Attacks
Chong Xiang, Prateek Mittal
State-of-the-art object detectors are vulnerable to localized patch hiding attacks, where an adversary introduces a small adversarial patch to make detectors miss the detection of…
PatchGuard: A Provably Robust Defense against Adversarial Patches via Small Receptive Fields and Masking
Chong Xiang, Arjun Nitin Bhagoji, Vikash Sehwag +1
Localized adversarial patches aim to induce misclassification in machine learning models by arbitrarily modifying pixels within a restricted region of an image. Such attacks can be…
Differentially Private Data Generative Models
Qingrong Chen, Chong Xiang, Minhui Xue +4
Deep neural networks (DNNs) have recently been widely adopted in various applications, and such success is largely due to a combination of algorithmic breakthroughs, computation re…
Generating 3D Adversarial Point Clouds
Chong Xiang, Charles R. Qi, Bo Li
Deep neural networks are known to be vulnerable to adversarial examples which are carefully crafted instances to cause the models to make wrong predictions. While adversarial examp…