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Object detection models, which are widely used in various domains (such asretail), have been shown to be vulnerable to adversarial attacks. Existingmethods for detecting adversarial attacks on object detectors have haddifficulty detecting new real-life attacks. We present X-Detect, a noveladversarial patch detector that can: i) detect adversarial samples in realtime, allowing the defender to take preventive action; ii) provide explanationsfor the alerts raised to support the defender's decision-making process, andiii) handle unfamiliar threats in the form of new attacks. Given a new scene,X-Detect uses an ensemble of explainable-by-design detectors that utilizeobject extraction, scene manipulation, and feature transformation techniques todetermine whether an alert needs to be raised. X-Detect was evaluated in boththe physical and digital space using five different attack scenarios (includingadaptive attacks) and the COCO dataset and our new Superstore dataset. Thephysical evaluation was performed using a smart shopping cart setup inreal-world settings and included 17 adversarial patch attacks recorded in 1,700adversarial videos. The results showed that X-Detect outperforms thestate-of-the-art methods in distinguishing between benign and adversarialscenes for all attack scenarios while maintaining a 0% FPR (no false alarms)and providing actionable explanations for the alerts raised. A demo isavailable.

X-Detect: Explainable Adversarial Patch Detection for Object Detectors in Retail