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• EvolveReason outperforms current state-of-the-art methods in deepfake identification performance.result 90%
• Traditional classification methods for deepfake identification lack explanatory ability.limitation 85%
• Explainable VLM approaches for deepfake identification often suffer from hallucinations and insufficient detail.limitation 85%
• EvolveReason incorporates a forgery latent-space distribution capture module that can identify high-frequency forgery cues difficult to extract from original images.technical 88%
• EvolveReason introduces a self-evolution exploration strategy using reinforcement learning to iteratively optimize textual descriptions.method 87%
• EvolveReason accurately identifies forgery details and demonstrates generalization capabilities.result 85%
• EvolveReason mimics the reasoning and observational processes of human auditors when identifying face forgeries.method 82%
• The CoT-Face dataset is constructed specifically for advanced VLMs to guide models to think in human-like ways.technical 83%

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