ODP-Net uses instance-aware orthogonal decomposition, perturbation-based purification, and manifold alignment to separate universal forgery traces, generator fingerprints, and semantics, achieving SOTA on unseen architectures like Stable Diffusion 3.
Dire for diffusion-generated image detection
6 Pith papers cite this work. Polarity classification is still indexing.
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MAFL uses adversarial training to suppress pattern and content biases, guiding models to learn shared generative features for better cross-model generalization in detecting AI images.
RW-Post is an auditable benchmark linking social media posts to evidence from human fact-check articles for evaluating multimodal AI fact-checking across different evidence regimes.
RW-Post is an auditable text-image benchmark for real-world multimodal fact-checking that links posts to evidence traces from human fact-check articles and includes the AgentFact baseline for evaluation.
FADNet reformulates face forgery detection as one-class learning on real faces only, using EDL uncertainty and a PFIG to achieve 96.63% average accuracy and 98.83% precision on DF40 and ASFD benchmarks.
LRD-Net achieves state-of-the-art cross-domain face forgery detection via a frequency-guided lightweight backbone and real-centered learning with only 2.63M parameters and substantially faster training and inference.
citing papers explorer
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Decoupling Semantics and Fingerprints: A Universal Representation for AI-Generated Image Detection
ODP-Net uses instance-aware orthogonal decomposition, perturbation-based purification, and manifold alignment to separate universal forgery traces, generator fingerprints, and semantics, achieving SOTA on unseen architectures like Stable Diffusion 3.
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Combating Pattern and Content Bias: Adversarial Feature Learning for Generalized AI-Generated Image Detection
MAFL uses adversarial training to suppress pattern and content biases, guiding models to learn shared generative features for better cross-model generalization in detecting AI images.
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RW-Post: Auditable Evidence-Grounded Multimodal Fact-Checking in the Wild
RW-Post is an auditable benchmark linking social media posts to evidence from human fact-check articles for evaluating multimodal AI fact-checking across different evidence regimes.
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RW-Post: Auditable Evidence-Grounded Multimodal Fact-Checking in the Wild
RW-Post is an auditable text-image benchmark for real-world multimodal fact-checking that links posts to evidence traces from human fact-check articles and includes the AgentFact baseline for evaluation.
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Only Train Once: Uncertainty-Aware One-Class Learning for Face Authenticity Detection
FADNet reformulates face forgery detection as one-class learning on real faces only, using EDL uncertainty and a PFIG to achieve 96.63% average accuracy and 98.83% precision on DF40 and ASFD benchmarks.
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LRD-Net: A Lightweight Real-Centered Detection Network for Cross-Domain Face Forgery Detection
LRD-Net achieves state-of-the-art cross-domain face forgery detection via a frequency-guided lightweight backbone and real-centered learning with only 2.63M parameters and substantially faster training and inference.