Quiet Triggers, Loud Footprints: A Tri-Space Measurement Study of Backdoor Stealthiness
Backdoor attacks on deep neural networks are commonly evaluated by attack success rate and benign accuracy, yet their stealthiness remains inconsistently defined and measured. Existing work often studies only one observable aspect of stealthiness, such as trigger imperceptibility, feature-space separability, parameter-space concentration, or evasion of particular defenses. This creates a fragmented picture: an attack may appear stealthy under one metric while leaving conspicuous footprints elsewhere. This paper presents a tri-space measurement study of backdoor stealthiness in image classification. We distinguish latent stealthiness---the ideal but unobservable difficulty of distinguishin...
| Authors | Behrad Tajalli, Henk Berendsen, Xiaoyun Xu et al. |
|---|---|
| Primary category | cs.CR |
| Published | 14 October 2026 |
| Last updated at source | 14 October 2026 |
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