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...