FakeReasoning is an MLLM-based framework for unified forgery detection and reasoning on AI-generated images, supported by the new MMFR-Dataset of 120K images and 378K annotations across 10 generators.
Deferred neural rendering: Image synthesis using neural textures
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A frequency-aware triple-branch network with mutual information-based decoupling and fusion losses achieves state-of-the-art deepfake detection across six benchmarks.
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Toward Generalizable Forgery Detection and Reasoning
FakeReasoning is an MLLM-based framework for unified forgery detection and reasoning on AI-generated images, supported by the new MMFR-Dataset of 120K images and 378K annotations across 10 generators.
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Unveiling Deepfakes: A Frequency-Aware Triple Branch Network for Deepfake Detection
A frequency-aware triple-branch network with mutual information-based decoupling and fusion losses achieves state-of-the-art deepfake detection across six benchmarks.