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AdvScene: Rethinking Adversarial Patch Evaluation Through Scene Robustness

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arXiv:2605. 30578v1 Announce Type: new Abstract: Adversarial patches are physical patterns attached to real objects to mislead AI vision systems.

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Authors: Xiaoyong (Brian), Yuan (Emily), Lan (Emily), Zhang
Released: 2026-06-01 (2mo ago)
Topic: Related to current project
Relation to my work: Read next because AdvScene: Rethinking Adversarial Patch Evaluation Through Scene Robustness overlaps with clean result "LoRA persona trained on <A> alone emits <B> at 23.5% when a co-trained partner learns <A>...<B>, vs 0% control on Qwen2.5-7B-Instruct (MODERATE confidence)", clean result "Leakage rate is a usable signal for recovering trigger-shaped phrases on Gaperon-1125-1B without knowing the hidden trigger itself (MODERATE confidence)", clean result "Language-mismatch LoRA SFT on Qwen2.5-7B leaks the trained completion language into bystander directives the model was never trained on, absent under same-language SFT (LOW confidence)". Matching terms: text, under, eval, control, trained. Source: arxiv cs.CR (Cryptography and Security).
arXiv: 2605.30578

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