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.
C2p-clip: Injecting category common prompt in clip to enhance generalization in deepfake detection
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PVLM combines parsing-aware vision-language modeling with dynamic contrastive learning to enable fine-grained zero-shot attribution of deepfakes to unseen generators and outperforms prior methods on a new benchmark.
MFVLR uses multi-domain vision-language reconstruction with a fine-grained language transformer, multi-domain vision encoder, and vision injection module to achieve generalizable detection and localization of diffusion-synthesized face forgeries.
citing papers explorer
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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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PVLM: Parsing-Aware Vision Language Model with Dynamic Contrastive Learning for Zero-Shot Deepfake Attribution
PVLM combines parsing-aware vision-language modeling with dynamic contrastive learning to enable fine-grained zero-shot attribution of deepfakes to unseen generators and outperforms prior methods on a new benchmark.
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MFVLR: Multi-domain Fine-grained Vision-Language Reconstruction for Generalizable Diffusion Face Forgery Detection and Localization
MFVLR uses multi-domain vision-language reconstruction with a fine-grained language transformer, multi-domain vision encoder, and vision injection module to achieve generalizable detection and localization of diffusion-synthesized face forgeries.