AdvScene: Rethinking Adversarial Patch Evaluation Through Scene Robustness
Adversarial patches are physical patterns attached to real objects to mislead AI vision systems. Their real-world risk is not determined by a single successful prediction, but by whether they remain effective after deployment under changing viewpoints, distances, and scene conditions. We refer to this property as scene robustness, the effectiveness of a deployed patch across conditions in a real environment. Yet existing evaluations do not measure scene robustness well: real image benchmarks are realistic but fixed, while simulators are controllable but not grounded in a specific real scene. We present AdvScene, a scene-grounded framework for measuring the scene robustness of adversarial ...
| Authors | Xiaoyong, Yuan, Lan et al. |
|---|---|
| Primary category | cs.CR |
| Published | 28 May 2026 |
| Last updated at source | 28 May 2026 |
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