Fluently Lying: Adversarial Robustness Can Be Substrate-Dependent
The primary tools used to monitor and defend object detectors under adversarial attack assume that when accuracy degrades, detection count drops in tandem. This coupling was assumed, not measured. We report a counterexample observed on a single model: under standard PGD, EMS-YOLO, a spiking neural network (SNN) object detector, retains more than 70% of its detections while mAP collapses from 0.528 to 0.042. We term this count-preserving accuracy collapse Quality Corruption (QC), to distinguish it from the suppression that dominates untargeted evaluation. Across four SNN architectures and two threat models (l-infinity and l-2), QC appears only in one of the four detectors tested (EMS-YOLO)...
| Authors | Daye Kang, Hyeongboo Baek |
|---|---|
| Primary category | cs.CV |
| Published | 1 April 2026 |
| Last updated at source | 16 June 2026 |
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