Robust Deepfake Detection: Mitigating Spatial Attention Drift via Calibrated Complementary Ensembles

ArXi:2604.25889v1 Announce Type: new Current deepfake detection models achieve state-of-the-art performance on pristine academic datasets but suffer severe spatial attention drift under real-world compound degradations, such as blurring and severe lossy compression. To address this vulnerability, we propose a foundation-driven forensic framework that integrates an extreme compound degradation engine with a structurally constrained, multi-stream architecture. During