Deepfake Detection in Social Media: A Temporal Artifact Analysis Using 3D Convolutional Neural Networks

• URL: http://arxiv.org/abs/2605.17573v1
• Date: Sun, 17 May 2026 18:01:32 GMT
• Title: Deepfake Detection in Social Media: A Temporal Artifact Analysis Using 3D Convolutional Neural Networks
• Abstract summary: Frame-level deepfake detectors degrade sharply as generator quality increases.<n>High-quality 128x128 GAN output cuts spatial-only accuracy by five percentage points.<n>We address this gap with a 3D Convolutional Neural Network detector based on R3D-18.
• Score: 2.1735205111264007
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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