Energy-based constraint networks learn structural coherence from contrastive pairs using frozen encoders, achieving 93.4% accuracy on text corruptions and 0.959 AUC on deepfake detection with composable branches that transfer across modalities via corruption respecification.
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PhyLAA-X embeds physics-derived feature volumes into localized artifact attention for improved cross-generator generalization and adversarial robustness in deepfake detection.
Fractal characterization of low-correlation signals distinguishes AI-generated images from real ones with claimed robustness and superior performance.
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Energy-Based Constraint Networks: Learning Structural Coherence Across Modalities
Energy-based constraint networks learn structural coherence from contrastive pairs using frozen encoders, achieving 93.4% accuracy on text corruptions and 0.959 AUC on deepfake detection with composable branches that transfer across modalities via corruption respecification.
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Aletheia: Physics-Conditioned Localized Artifact Attention (PhyLAA-X) for End-to-End Generalizable and Robust Deepfake Video Detection
PhyLAA-X embeds physics-derived feature volumes into localized artifact attention for improved cross-generator generalization and adversarial robustness in deepfake detection.
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Fractal Characterization of Low-Correlation Signals in AI-Generated Image Detection
Fractal characterization of low-correlation signals distinguishes AI-generated images from real ones with claimed robustness and superior performance.