CVPR 2023 - Jedi: Entropy-based Localization and Removal of Adversarial Patches

In this episode we discuss Jedi: Entropy-based Localization and Removal of Adversarial Patches by Bilel Tarchoun, Anouar Ben Khalifa, Mohamed Ali Mahjoub, Nael Abu-Ghazaleh, Ihsen Alouani. The paper proposes a new defense against adversarial patches that is resilient to realistic patch attacks, called Jedi. Jedi tackles the patch localization problem from an information theory perspective using two new ideas: using entropy analysis to improve the identification of potential patch regions, and an autoencoder to improve the localization of adversarial patches. Jedi achieves high-precision adversarial patch localization, and can be applied on pre-trained off-the-shelf models without changes to their training or inference. It detects on average 90% of adversarial patches and recovers up to 94% of successful patch attacks.