✨Training-free Detection of Generated Videos via Spatial-Temporal Likelihoods

📝 Summary:
STALL is a training-free, model-agnostic detector for generated videos. It jointly models spatial and temporal evidence from real-data statistics within a probabilistic framework. STALL consistently outperforms prior image and video-based baselines, improving reliable detection.

🔹 Publication Date: Published on Mar 16

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.15026
• PDF: https://arxiv.org/pdf/2603.15026
• Project Page: https://omerbenhayun.github.io/stall-video/
• Github: https://github.com/OmerBenHayun/stall-video

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For more data science resources:
✓ https://xn--r1a.website/DataScienceT

#Deepfakes #VideoDetection #ComputerVision #AI #DigitalForensics

✨ArtifactNet: Detecting AI-Generated Music via Forensic Residual Physics

📝 Summary:
ArtifactNet detects AI-generated music by analyzing codec-specific artifacts in audio signals using a lightweight neural network and codec-aware training. It achieves superior performance and efficiency compared to existing methods, establishing forensic physics as a new detection paradigm.

🔹 Publication Date: Published on Apr 17

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.16254
• PDF: https://arxiv.org/pdf/2604.16254
• Project Page: https://demo.intrect.io

🔹 Models citing this paper:
• https://huggingface.co/intrect/artifactnet

✨ Datasets citing this paper:
• https://huggingface.co/datasets/intrect/artifactbench

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For more data science resources:
✓ https://xn--r1a.website/DataScienceT

#AI #MachineLearning #AIMusic #DigitalForensics #AudioProcessing