M3D-Net: Multi-Modal 3D Facial Feature Reconstruction Network for Deepfake Detection

With the rapid advancement of deep learning in image generation, facial forgery techniques have achieved unprecedented realism, posing serious threats to cybersecurity and information authenticity. Most existing deepfake detection approaches rely on the reconstruction of isolated facial attributes without fully exploiting the complementary nature of multi-modal feature representations. To address these challenges, this paper proposes a novel Multi-Modal 3D Facial Feature Reconstruction Network (M3D-Net) for deepfake detection. Our method leverages an end-to-end dual-stream architecture that reconstructs fine-grained facial geometry and reflectance properties from single-view RGB images via a self-supervised 3D facial reconstruction module. The network further enhances detection performance through a 3D Feature Pre-fusion Module (PFM), which adaptively adjusts multi-scale features, and a Multi-modal Fusion Module (MFM) that effectively integrates RGB and 3D-reconstructed features using attention mechanisms. Extensive experiments on multiple public datasets demonstrate that our approach achieves state-of-the-art performance in terms of detection accuracy and robustness, significantly outperforming existing methods while exhibiting strong generalization across diverse scenarios.

Key Contributions

• Dual-stream architecture combining 3D facial reconstruction (albedo + depth) with RGB features for deepfake detection
• 3D Feature Pre-fusion Module (PFM) using depthwise separable convolutions and spatial kernel attention for multi-scale 3D feature integration
• Multi-modal Fusion Module (MFM) with attention mechanisms for deep integration of RGB and 3D-reconstructed features

🛡️ Threat Analysis

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