Architecture-Adaptive Uncertainty Fusion for Deepfake Detection 事件

Architecture-Adaptive Uncertainty Fusion for Deepfake Detection arXiv:2606.06666v1 Announce Type: new Abstract: Deepfake detection systems achieve near-perfect accuracy on benchmarks, yet forensic deployment demands reliable prediction uncertainty. Existing uncertainty quantification (UQ) methods rely on single sources and ignore that optimal uncertainty composition varies across architectures. We propose Correlation-Optimized Fusion (COF), an architecture-adaptive framework that fuses five com