EvolveReason: Self-Evolving Reasoning Paradigm for Explainable Deepfake Facial Image Identification

TL;DR

The proposed EvolveReason mimics the reasoning and observational processes of human auditors when identifying face forgeries, and introduces a self-evolution exploration strategy to allow the model to iteratively explore and optimize its textual descriptions in a two-stage process.

Abstract

With the rapid advancement of AIGC technology, developing identification methods to address the security challenges posed by deepfakes has become urgent. Face forgery identification techniques can be categorized into two types: traditional classification methods and explainable VLM approaches. The former provides classification results but lacks explanatory ability, while the latter, although capable of providing coarse-grained explanations, often suffers from hallucinations and insufficient detail. To overcome these limitations, we propose EvolveReason, which mimics the reasoning and observational processes of human auditors when identifying face forgeries. By constructing a chain-of-thought dataset, CoT-Face, tailored for advanced VLMs, our approach guides the model to think in a human-like way, prompting it to output reasoning processes and judgment results. This provides practitioners with reliable analysis and helps alleviate hallucination. Additionally, our framework incorporates a forgery latent-space distribution capture module, enabling EvolveReason to identify high-frequency forgery cues difficult to extract from the original images. To further enhance the reliability of textual explanations, we introduce a self-evolution exploration strategy, leveraging reinforcement learning to allow the model to iteratively explore and optimize its textual descriptions in a two-stage process. Experimental results show that EvolveReason not only outperforms the current state-of-the-art methods in identification performance but also accurately identifies forgery details and demonstrates generalization capabilities.

Figures (5)

• Figure 1: Comparison of our method (b) against other existing explainable approaches (a) in face forgery identification.
• Figure 2: The framework of the proposed EvolveReason. During training, the forgery distribution latent space feature extraction is used to enhance high-frequency forgery clues in the input of forged images and restore detailed information. Subsequently, fine-tuning the VLM with the forgery identification knowledge guided by the chain-of-thought in the CoT-Face dataset encourages the model to mimic the real auditor's process of observing and identifying forged images. Finally, reinforcement learning is employed to self-evolve the VLM's text explanation ability, enhancing the reliability of explanations and identification.
• Figure 3: The overall workflow and schematic diagram of self-evolution reasoning.
• Figure 4: Qualitative examples of results on certain samples. For failure cases of Qwen3-235B-A22B and GPT-4o, EvolveReason achieves correct classification results along with corresponding explanations.
• Figure 5: Generalization performance of EvolveReason versus the in-domain performance of existing methods. The radar chart illustrates identification performance across different forgery categories: solid lines represent baseline methods, while the dashed line corresponds to EvolveReason. The bar chart reports mean AUC for each approach. All six baseline methods were trained and evaluated on DeepFaceGen, whereas CorrDetail and EvolveReason were trained on FF++ and tested on DeepFaceGen, representing a cross-dataset evaluation.