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SentiNet is a novel detection framework for localized universal attacks onneural networks. These attacks restrict adversarial noise to contiguousportions of an image and are reusable with different images -- constraints thatprove useful for generating physically-realizable attacks. Unlike most otherworks on adversarial detection, SentiNet does not require training a model orpreknowledge of an attack prior to detection. Our approach is appealing due tothe large number of possible mechanisms and attack-vectors that anattack-specific defense would have to consider. By leveraging the neuralnetwork's susceptibility to attacks and by using techniques from modelinterpretability and object detection as detection mechanisms, SentiNet turns aweakness of a model into a strength. We demonstrate the effectiveness ofSentiNet on three different attacks -- i.e., data poisoning attacks, trojanednetworks, and adversarial patches (including physically realizable attacks) --and show that our defense is able to achieve very competitive performancemetrics for all three threats. Finally, we show that SentiNet is robust againststrong adaptive adversaries, who build adversarial patches that specificallytarget the components of SentiNet's architecture.

SentiNet: Detecting Localized Universal Attacks Against Deep Learning Systems