COMPASS: Contextual Moment Prior Alignment for Generalizable AIGI Detection

Key points

• Generalizable AI-generated image (AIGI) and deepfake detection remain challenging due to the asymmetry phenomenon, where adaptation to limited training distributions can degrade the transferable representations of Vision Foundation Models (VFMs).1
• Recent subspace adaptation methods mitigate this issue by freezing high-magnitude principal components while adapting the residual subspace.1
• COMPASS incorporates activation covariance into the decomposition of pre-trained weights, preserving target-relevant representations as a stable semantic reference while the residual adapter learns forgery-related variations.1
• Extensive experiments demonstrate strong generalization across cross-dataset, cross-method, joint cross-dataset-method, and open-world cross-generator evaluations, achieving state-of-the-art performance on both Deepfake and AIGI detection benchmarks.1

Sources (1)

• [1]COMPASS: Contextual Moment Prior Alignment for Generalizable AIGI Detection
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 13, 09:23 AM

  We propose COMPASS (COntextual Moment Prior Alignment for Subspace Separation), a data-aware subspace adaptation framework that uses a small unlabeled Semantic Proxy Set to estimate target activation statistics.

  Generalizable AI-generated image (AIGI) and deepfake detection remain challenging due to the asymmetry phenomenon, where adaptation to limited training distributions can degrade the transferable representations of Vision Foundation Models (VFMs).

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