Jun 22, 2023 � We show that the models trained with our Greedy Cutout improves certified robust accuracy over Random Cutout in PatchCleanser across a range of�...
Oct 1, 2024 � Revisiting Image Classifier Training for Improved Certified Robust Defense against Adversarial Patches � Aniruddha Saha, Shuhua Yu, Mohammad�...
Revisiting Image Classifier Training for Improved Certified Robust Defense against Adversarial Patches � Aniruddha Saha, Shuhua Yu, +2 authors. Chaithanya Kumar�...
Certifiably robust defenses against adversarial patches for image classifiers ensure correct prediction against any changes to a constrained neighborhood of�...
PatchZero: Defending against Adversarial Patch Attacks by Detecting and Zeroing the Patch � Revisiting Image Classifier Training for Improved Certified Robust�...
PatchCleanser achieves 83.9% top-1 clean accuracy and 62.1% top-1 certified robust accuracy against a 2%-pixel square patch anywhere on the image.
Missing: Revisiting Training
Revisiting Image Classifier Training for Improved Certified Robust Defense against Adversarial Patches. TMLR. better training technique for PatchCleanser�...
We present PatchCleanser's double-masking defense that is compatible with any image classifier to mitigate the threat of adversarial patch attacks. • We�...
Missing: Revisiting | Show results with:Revisiting
Sep 5, 2024 � To counter this threat, we propose PatchCleanser as a certifiably robust defense against adversarial patches that is compatible with any image�...
Certifiably robust defenses against adversarial patches for image classifiers ensure correct prediction against any changes to a constrained neighborhood of�...