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<p>The spread of deepfake videos, created by the application of sophisticated artificial intelligence technologies, presents major hazards to public confidence and digital integrity. To analyze spatial features in individual frames, current deepfake detection techniques mostly depend on convolutional neural networks (CNNs). However, they often overlook temporal dependencies crucial for detecting subtle alterations. We introduce GraphNeXt, a unified framework that combines the spatial representation power of ResNeXt with the temporal modeling capabilities of graph neural networks (GNNs). By integrating three GNN variants (GCN, GAT, and GraphSAGE) with ResNeXt, our approach jointly captures spatial and temporal patterns in video sequences. Our models have been extensively tested on DFDC and Google DFD benchmarks, showcasing superior performance compared to traditional CNN-based approaches, ensuring reliable deepfake detection across a wide range of video origins. These findings underscore the significant role of hybrid CNN-GNN frameworks in progressing video forensics and mitigating the risks posed by synthetic media.</p>

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GraphNeXt: a unified CNN-GNN framework for deepfake video detection

  • 期刊论文

  • 发表日期：

    2026年4月28日

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  • Dev Bhuva,
  • Manav Shah,
  • Manav Abhang,
  • Akash Kumar,
  • Sandip Shingade,
  • Sushila Shelke

摘要

The spread of deepfake videos, created by the application of sophisticated artificial intelligence technologies, presents major hazards to public confidence and digital integrity. To analyze spatial features in individual frames, current deepfake detection techniques mostly depend on convolutional neural networks (CNNs). However, they often overlook temporal dependencies crucial for detecting subtle alterations. We introduce GraphNeXt, a unified framework that combines the spatial representation power of ResNeXt with the temporal modeling capabilities of graph neural networks (GNNs). By integrating three GNN variants (GCN, GAT, and GraphSAGE) with ResNeXt, our approach jointly captures spatial and temporal patterns in video sequences. Our models have been extensively tested on DFDC and Google DFD benchmarks, showcasing superior performance compared to traditional CNN-based approaches, ensuring reliable deepfake detection across a wide range of video origins. These findings underscore the significant role of hybrid CNN-GNN frameworks in progressing video forensics and mitigating the risks posed by synthetic media.