Deepfake Detection in Social Media: A Temporal Artifact Analysis Using 3D Convolutional Neural Networks
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| Format: | Preprint |
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2026
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| author | Rashidi, Mohammadreza Ali, Raja Hashim Rahman, Sami Ur |
| author_facet | Rashidi, Mohammadreza Ali, Raja Hashim Rahman, Sami Ur |
| contents | Synthetic facial videos have proliferated across social media faster than platform moderation can respond, raising the cost of disinformation and identity-based attacks. Frame-level deepfake detectors degrade sharply as generator quality increases; high-quality 128x128 GAN output cuts spatial-only accuracy by five percentage points while leaving temporal inconsistencies largely intact. We address this gap with a 3D Convolutional Neural Network detector based on R3D-18, trained with a composite loss that combines binary cross-entropy with a temporal-consistency regularizer. The model processes 16-frame clips from the DeepfakeTIMIT dataset and is initialized from Kinetics-400 action-recognition weights. We report 92.8% accuracy on intra-dataset evaluation at 128x128 resolution; cross-dataset transfer to FaceForensics++ without fine-tuning reaches 76.4%, rising after minimal fine-tuning. Ablation studies show that transfer learning contributes 7.2 percentage points and face tracking adds 3.5 points, while temporal consistency regularization provides additional gains on high-quality fakes. The results establish that temporal artifacts generalize more broadly than spatial ones, providing a detection signal that survives social-media re-encoding. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2605_17573 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Deepfake Detection in Social Media: A Temporal Artifact Analysis Using 3D Convolutional Neural Networks Rashidi, Mohammadreza Ali, Raja Hashim Rahman, Sami Ur Computer Vision and Pattern Recognition Cryptography and Security I.4.9; I.2.10; H.3.1 Synthetic facial videos have proliferated across social media faster than platform moderation can respond, raising the cost of disinformation and identity-based attacks. Frame-level deepfake detectors degrade sharply as generator quality increases; high-quality 128x128 GAN output cuts spatial-only accuracy by five percentage points while leaving temporal inconsistencies largely intact. We address this gap with a 3D Convolutional Neural Network detector based on R3D-18, trained with a composite loss that combines binary cross-entropy with a temporal-consistency regularizer. The model processes 16-frame clips from the DeepfakeTIMIT dataset and is initialized from Kinetics-400 action-recognition weights. We report 92.8% accuracy on intra-dataset evaluation at 128x128 resolution; cross-dataset transfer to FaceForensics++ without fine-tuning reaches 76.4%, rising after minimal fine-tuning. Ablation studies show that transfer learning contributes 7.2 percentage points and face tracking adds 3.5 points, while temporal consistency regularization provides additional gains on high-quality fakes. The results establish that temporal artifacts generalize more broadly than spatial ones, providing a detection signal that survives social-media re-encoding. |
| title | Deepfake Detection in Social Media: A Temporal Artifact Analysis Using 3D Convolutional Neural Networks |
| topic | Computer Vision and Pattern Recognition Cryptography and Security I.4.9; I.2.10; H.3.1 |
| url | https://arxiv.org/abs/2605.17573 |