Adaptive Equilibrium: Dynamic Weighting Framework for Generalized Interruption of DeepFake Models
The advancement of generalized deepfake disruption is constrained by the interruption imbalance, a fundamental bottleneck inherent to the generation of universal perturbations. We reveal that conventional static gradient normalization fundamentally struggles to resolve architectural conflicts, causing the optimization to bias towards susceptible models while neglecting resistant ones. We argue that achieving high and uniform effectiveness requires resolving this imbalance by reaching an adaptive equilibrium. We propose the Adaptive Equilibrium Framework (AEF), which employs a dynamic weighting mechanism that utilizes real-time loss feedback to adaptively assign greater interruption weight...
| Authors | Hongrui Zheng, Liejun Wang, Zhiqing Guo |
|---|---|
| Primary category | cs.LG |
| Published | 1 May 2026 |
| Last updated at source | 1 May 2026 |
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