CMTA detects AI-generated videos by capturing unnatural temporal stability in visual-textual semantic alignment via joint embeddings and multi-grained temporal modeling, outperforming prior methods in cross-generator tests.
Show-1: Marrying pixel and latent diffusion models for text-to-video generation
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ATSS detects AI-generated videos by measuring unnatural repetitive temporal correlations in triple similarity matrices derived from frame visuals and semantic descriptions.
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CMTA: Leveraging Cross-Modal Temporal Artifacts for Generalizable AI-Generated Video Detection
CMTA detects AI-generated videos by capturing unnatural temporal stability in visual-textual semantic alignment via joint embeddings and multi-grained temporal modeling, outperforming prior methods in cross-generator tests.
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ATSS: Detecting AI-Generated Videos via Anomalous Temporal Self-Similarity
ATSS detects AI-generated videos by measuring unnatural repetitive temporal correlations in triple similarity matrices derived from frame visuals and semantic descriptions.