DCA measures intra-sample representational consistency in frozen vision models by checking per-dimension coactivation across regions, achieving 0.91-0.93 AUC in deepfake detection with DINOv3 features.
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Orthogonal subspace decomposition via SVD on vision foundation model features preserves high-rank pre-trained knowledge by freezing principal components and adapting residuals, reducing overfitting for better generalization in AI-generated image detection.
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Dimensional Coactivation for Representational Consistency in Frozen Vision Foundation Models
DCA measures intra-sample representational consistency in frozen vision models by checking per-dimension coactivation across regions, achieving 0.91-0.93 AUC in deepfake detection with DINOv3 features.
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Orthogonal Subspace Decomposition for Generalizable AI-Generated Image Detection
Orthogonal subspace decomposition via SVD on vision foundation model features preserves high-rank pre-trained knowledge by freezing principal components and adapting residuals, reducing overfitting for better generalization in AI-generated image detection.