Real-world adversarial physical patches were recently shown to be successful in compromising state-of-the-art models in a variety of computer vision applications. The most promising defenses that are based on either input gradient or features analyses have been shown to be compromised by recent GAN-based adaptive attacks that generate realistic/naturalistic patches. In this paper, we propose Jedi, a new defense against adversarial patches that is resilient to realistic patch attacks, and also improves detection and recovery compared to the state of the art. Jedi leverages two new ideas: (1) it improves the identification of potential patch regions using entropy analysis: we show that the entropy of adversarial patches is high, even in naturalistic patches; and (2) it improves the localization of adversarial patches, using an autoencoder that is able to complete patch regions and filter out normal regions with high entropy that are not part of a patch. Jedi achieves high precision adversarial patch localization, which we show is critical to successfully repair the images. Since Jedi relies on an input entropy analysis, it is model-agnostic, and can be applied on pre-trained off-the-shelf models without changes to the training or inference of the protected models. Jedi detects on average 90% of adversarial patches across different benchmarks and recovers up to 94% of successful patch attacks (Compared to 75% and 65% for LGS and Jujutsu, respectively). Jedi is also able to continue detection even in the presence of adaptive realistic patches that are able to fool other defenses.