382 citations · 2.5k across the 96 of their papers we have counts for
11 papers · 1 filter
Differentially Private Image Classification by Learning Priors from Random Processes
Xinyu Tang, Ashwinee Panda, Vikash Sehwag +1
In privacy-preserving machine learning, differentially private stochastic gradient descent (DP-SGD) performs worse than SGD due to per-sample gradient clipping and noise addition.…
A Light Recipe to Train Robust Vision Transformers
Edoardo Debenedetti, Vikash Sehwag, Prateek Mittal
In this paper, we ask whether Vision Transformers (ViTs) can serve as an underlying architecture for improving the adversarial robustness of machine learning models against evasion…
Just Rotate it: Deploying Backdoor Attacks via Rotation Transformation
Tong Wu, Tianhao Wang, Vikash Sehwag +2
Recent works have demonstrated that deep learning models are vulnerable to backdoor poisoning attacks, where these attacks instill spurious correlations to external trigger pattern…
ObjectSeeker: Certifiably Robust Object Detection against Patch Hiding Attacks via Patch-agnostic Masking
Chong Xiang, Alexander Valtchanov, Saeed Mahloujifar +1
Object detectors, which are widely deployed in security-critical systems such as autonomous vehicles, have been found vulnerable to patch hiding attacks. An attacker can use a sing…
PatchCleanser: Certifiably Robust Defense against Adversarial Patches for Any Image Classifier
Chong Xiang, Saeed Mahloujifar, Prateek Mittal
The adversarial patch attack against image classification models aims to inject adversarially crafted pixels within a restricted image region (i.e., a patch) for inducing model mis…
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…