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Creators/Authors contains: "Man, Yanmao"

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  1. Free, publicly-accessible full text available August 9, 2024
  2. Modern autonomous systems rely on both object detection and object tracking in their visual perception pipelines. Although many recent works have attacked the object detection component of autonomous vehicles, these attacks do not work on full pipelines that integrate object tracking to enhance the object detector's accuracy. Meanwhile, existing attacks against object tracking either lack real-world applicability or do not work against a powerful class of object trackers, Siamese trackers. In this paper, we present AttrackZone, a new physically-realizable tracker hijacking attack against Siamese trackers that systematically determines valid regions in an environment that can be used for physical perturbations. AttrackZone exploits the heatmap generation process of Siamese Region Proposal Networks in order to take control of an object's bounding box, resulting in physical consequences including vehicle collisions and masked intrusion of pedestrians into unauthorized areas. Evaluations in both the digital and physical domain show that AttrackZone achieves its attack goals 92% of the time, requiring only 0.3-3 seconds on average. 
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  3. Adversarial examples in deep learning were first discovered for the digital domain and later effected in the physical domain. In this work, we demonstrate adversarial examples in the perception domain; i.e., adversarial examples that are introduced by compromising the sensing mechanism of an image sensor. Our proposed attack relies on the injection of electromagnetic interference in a remote manner, and does not require physical modifications to the object, which makes the attack easier to launch and harder to detect. 
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