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Abstract

Adversarial patches pose a practical threat to modern object detectors. Prior work shows vulnerability, but three gaps limit actionable insight: (i) few score-based black-box attacks jointly optimize patch location, texture, and size under tight query budgets; (ii) success is rarely tied to the patchs visual footprint ; and (iii) evaluations often conflate EOT robustness with plain-view suppression. We present PATCHBANDIT, a query-efficient, budget-adaptive black-box attack that couples a lightweight Contextual Thompson-Sampling placer with NES-style pixel updates, growing the patch only when progress stalls. Reporting is anchored by a strict plain-image suppression test; EOT is audited but never used as a substitute for success, and optional appearance/printability weights expose strengthvisibility trade-offs. Across YOLOv5, Faster R-CNN, and YOLOS, PATCHBANDIT achieves strong suppression on CNN-based detectors and substantial suppression on the transformer-based detector, using compact patches and exposing clear queryfootprint trade-offs relative to fixed-size and heuristic baselines. A printcapture pilot further shows transfer across unseen physical objects and viewpoints. Code is available at https://github.com/pedram-mohaj er/PatchBandit.