Improving Adversarial Robustness via Activation Amplification and Attenuation
The existence of adversarial attacks is often attributed to the presence of non-robust features in neural networks. While prior defenses reduce their impact via pruning, masking, or feature recalibration, we instead propose to jointly learn to amplify and attenuate these signals through a simple activation scaling mechanism. To this end, we introduce Activation Amplification and Attenuation (A3), a lightweight plug-in module that enhances adversarial robustness with minimal modifications of the activations. A3 dynamically rescales the activations using a learnable mask and a scaling factor derived from the original activation magnitudes. The influence of adversarial perturbations can be a...
| Authors | Taïga Gonçalves, Yongsong Huang, Tomo Miyazaki et al. |
|---|---|
| Primary category | cs.CV |
| Published | 26 June 2026 |
| Last updated at source | 26 June 2026 |
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