IPatch: A Remote Adversarial Patch

Understand

Applications such as autonomous vehicles and medical screening use deep learning models to localize and identify hundreds of objects in a single frame.

• In the past, it has been shown how an attacker can fool these models by placing an adversarial patch within a scene.
• However, these patches must be placed in the target location and do not explicitly alter the semantics elsewhere in the image.
• In this paper, we introduce a new type of adversarial patch which alters a model's perception of an image's semantics.