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DNN-based video object detection (VOD) powers autonomous driving and videosurveillance industries with rising importance and promising opportunities.However, adversarial patch attack yields huge concern in live vision tasksbecause of its practicality, feasibility, and powerful attack effectiveness.This work proposes Themis, a software/hardware system to defend againstadversarial patches for real-time robust video object detection. We observethat adversarial patches exhibit extremely localized superficial featureimportance in a small region with non-robust predictions, and thus propose theadversarial region detection algorithm for adversarial effect elimination.Themis also proposes a systematic design to efficiently support the algorithmby eliminating redundant computations and memory traffics. Experimental resultsshow that the proposed methodology can effectively recover the system from theadversarial attack with negligible hardware overhead.

Real-Time Robust Video Object Detection System Against Physical-World Adversarial Attacks