🤖 AI Summary
This work addresses a critical limitation in existing physical adversarial attack research, which predominantly relies on single-frame image evaluations and fails to capture the robustness of real-world surveillance systems under temporal continuity, multi-sensor fusion, and practical deployment constraints. To bridge this gap, the authors propose a four-dimensional evaluation framework tailored for surveillance systems, encompassing temporal identity consistency, visible-infrared dual-modality evasion, controllable wearable carrier feasibility, and system-level target alignment. By integrating multi-object tracking, dual-modality adversarial sample generation, and real-world deployment tests, the study exposes the inadequacy of single-frame assessments and underscores the necessity of system-level robustness validation across time, modalities, and realistic operational conditions. It further highlights key challenges such as distance-dependent robustness and discrepancies in camera processing pipelines.
📝 Abstract
Physical adversarial attacks are increasingly studied in settings that resemble deployed surveillance systems rather than isolated image benchmarks. In these settings, person detection, multi-object tracking, visible--infrared sensing, and the practical form of the attack carrier all matter at once. This changes how the literature should be read. A perturbation that suppresses a detector in one frame may have limited practical effect if identity is recovered over time; an RGB-only result may say little about night-time systems that rely on visible and thermal inputs together; and a conspicuous patch can imply a different threat model from a wearable or selectively activated carrier. This paper reviews physical attacks from that surveillance-oriented viewpoint. Rather than attempting a complete catalogue of all physical attacks in computer vision, we focus on the technical questions that become central in surveillance: temporal persistence, sensing modality, carrier realism, and system-level objective. We organize prior work through a four-part taxonomy and discuss how recent results on multi-object tracking, dual-modal visible--infrared evasion, and controllable clothing reflect a broader change in the field. We also summarize evaluation practices and unresolved gaps, including distance robustness, camera-pipeline variation, identity-level metrics, and activation-aware testing. The resulting picture is that surveillance robustness cannot be judged reliably from isolated per-frame benchmarks alone; it has to be examined as a system problem unfolding over time, across sensors, and under realistic physical deployment constraints.