Statistical Adversaries: Natural Backdoor-like Adversarial Features in Clean Vision Datasets
Model-specific adversarial attacks have been extensively studied. We study a different failure mode: naturally occurring statistical signals in vision data that can behave as backdoor-like triggers without being maliciously inserted. We call these signals statistical adversaries. We analyse ImageNet to find patterns that are strongly linked to certain labels. We then use statistical controls to remove random correlations from our candidate signals. Finally, we demonstrate that these signals directly and predictably alter model predictions. These statistical adversaries are more targeted than generic corruptions and transfer across different model architectures. This suggests that some vul...
| Authors | Paul K. Mandal, Pavan Reddy, Tristan Malatynski |
|---|---|
| Primary category | cs.CV |
| Published | 6 July 2026 |
| Last updated at source | 17 August 2026 |
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