ForensicFlow: A Tri-Modal Adaptive Network for Robust Deepfake Detection

• URL: http://arxiv.org/abs/2511.14554v1
• Date: Tue, 18 Nov 2025 14:56:34 GMT
• Title: ForensicFlow: A Tri-Modal Adaptive Network for Robust Deepfake Detection
• Authors: Mohammad Romani,
• Abstract summary: We introduce ForensicFlow, a tri-modal forensic framework that fuses RGB, texture, and frequency evidence for video Deepfake detection.<n>Trained on Celeb-DF (v2) with Focal Loss, ForensicFlow achieves AUC 0.9752, F1-Score 0.9408, and accuracy 0.9208, outperforming single-stream baselines.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Deepfakes generated by advanced GANs and autoencoders severely threaten information integrity and societal stability. Single-stream CNNs fail to capture multi-scale forgery artifacts across spatial, texture, and frequency domains, limiting robustness and generalization. We introduce the ForensicFlow, a tri-modal forensic framework that synergistically fuses RGB, texture, and frequency evidence for video Deepfake detection. The RGB branch (ConvNeXt-tiny) extracts global visual inconsistencies; the texture branch (Swin Transformer-tiny) detects fine-grained blending artifacts; the frequency branch (CNN + SE) identifies periodic spectral noise. Attention-based temporal pooling dynamically prioritizes high-evidence frames, while adaptive attention fusion balances branch contributions.Trained on Celeb-DF (v2) with Focal Loss, ForensicFlow achieves AUC 0.9752, F1-Score 0.9408, and accuracy 0.9208, outperforming single-stream baselines. Ablation validates branch synergy; Grad-CAM confirms forensic focus. This comprehensive feature fusion provides superior resilience against subtle forgeries.

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