Fake3DGS benchmark shows state-of-the-art 2D fake detectors fail on 3D-manipulated Gaussian Splatting images while a new multi-view coherence method improves detection.
arXiv preprint arXiv:1909.06122 , year=
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LAA-X uses multi-task learning with explicit localized artifact attention and blending synthesis to build a deepfake detector that generalizes to high-quality and unseen manipulations after training only on real and pseudo-fake samples.
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.
citing papers explorer
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Fake3DGS: A Benchmark for 3D Manipulation Detection in Neural Rendering
Fake3DGS benchmark shows state-of-the-art 2D fake detectors fail on 3D-manipulated Gaussian Splatting images while a new multi-view coherence method improves detection.
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LAA-X: Unified Localized Artifact Attention for Quality-Agnostic and Generalizable Face Forgery Detection
LAA-X uses multi-task learning with explicit localized artifact attention and blending synthesis to build a deepfake detector that generalizes to high-quality and unseen manipulations after training only on real and pseudo-fake samples.
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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.