Uncertainty-Aware Deepfake Detection via Multi-View Structural Learning

• URL: http://arxiv.org/abs/2607.28769v1
• Date: Thu, 30 Jul 2026 18:42:01 GMT
• Title: Uncertainty-Aware Deepfake Detection via Multi-View Structural Learning
• Abstract summary: Security-critical biometric and forensic applications require accurate predictions and reliable confidence estimates, particularly under distribution shift.<n>We propose an uncertainty-aware deepfake detection framework that identifies manipulations through inconsistencies across complementary evidence sources.<n>We introduce Inter-Branch Disagreement (IBDC), a disagreement-aware uncertainty modeling mechanism that links predictive uncertainty to conflicts among evidence streams.
• Score: 6.951909224842812
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Security-critical biometric and forensic applications require accurate predictions and reliable confidence estimates, particularly under distribution shift. This challenge is especially acute for deepfake detection, where foundation-model-based detectors often exhibit overconfident predictions on out-of-distribution manipulations, which limits their suitability for operational deployment. We propose an uncertainty-aware deepfake detection framework that identifies manipulations through inconsistencies across complementary evidence sources. The framework integrates three streams: a visual stream based on an adapted CLIP encoder, a semantic stream that models consistency among facial attributes through differentiable constraints, and a structural stream that captures class-dependent dependency patterns between semantic and forensic features. To effectively combine these signals, we introduce Inter-Branch Disagreement Calibration (IBDC), a disagreement-aware uncertainty modeling mechanism that links predictive uncertainty to conflicts among evidence streams. Extensive cross-dataset experiments using FaceForensics++ as the training source demonstrate that the proposed framework achieves state-of-the-art generalization across multiple out-of-distribution benchmarks while consistently improving calibration and selective prediction performance. These results show that combining complementary evidence with disagreement-aware uncertainty provides a robust foundation for trustworthy and well-calibrated deepfake detection under distribution shift.

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