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 & tables
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.
Table 1: Comparison of EvolveReason with SOTA methods across both intra- and cross-datasets. The highest and second-highest performances are highlighted in best and second best , respectively. And / denotes methods that fail to generate interpretable text. Moreover, ACC and AUC denote that higher values are preferred, while EER signifies that lower values are desired. All scores are expressed in %.
Module
Intra-Testing
Cross-Testing
FVCE
ICA
SER
LQ
HQ
CelebDF
DFDC
✗
✗
✗
88.44
91.87
65.58
69.42
✗
✗
✔
91.91
94.59
71.03
74.97
✗
✔
✗
92.75
96.15
73.11
76.59
✔
✗
✗
91.20
94.81
68.59
74.31
✗
✔
✔
96.11
98.43
77.39
78.28
Table 2: Ablation study of EvolveReason with FF++ datasets. Cross-domain testing is conducted on LQ. Best are highlighted in bold . All numbers are AUC (%).
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.
Multimodal large language models (MLLMs) offer a promising path toward interpretable deepfake detection by generating textual explanations. However, the reasoning process of current MLLM-based methods combines evidence generation and manipulation localization into a unified step. This combination blurs the boundary between faithful observations and hallucinated explanations, leading to unreliable conclusions. Building on this, we present VIGIL, a part-centric structured forensic framework inspired by expert forensic practice through a plan-then-examine pipeline: the model first plans which facial parts warrant inspection based on global visual cues, then examines each part with independently sourced forensic evidence. A stage-gated injection mechanism delivers part-level forensic evidence only during examination, ensuring that part selection remains driven by the model's own perception rather than biased by external signals. We further propose a progressive three-stage training paradigm whose reinforcement learning stage employs part-aware rewards to enforce anatomical validity and evidence--conclusion coherence. To enable rigorous generalizability evaluation, we construct OmniFake, a hierarchical 5-Level benchmark where the model, trained on only three foundational generators, is progressively tested up to in-the-wild social-media data. Extensive experiments on OmniFake and cross-dataset evaluations demonstrate that VIGIL consistently outperforms both expert detectors and concurrent MLLM-based methods across all generalizability levels.
Xinghan Li, Junhao Xu, Jingjing Chen
College of Computer Science and Artificial Intelligence, Fudan University, Shanghai, China.
Recent Deepfake Video Detection (DFD) studies have demonstrated that pre-trained Vision-Language Models (VLMs) such as CLIP exhibit strong generalization capabilities in detecting artifacts across different identities. However, existing approaches focus on leveraging visual features only, overlooking their most distinctive strength -- the rich vision-language semantics embedded in the latent space. We propose VLAForge, a novel DFD framework that unleashes the potential of such cross-modal semantics to enhance model's discriminability in deepfake detection. This work i) enhances the visual perception of VLM through a ForgePerceiver, which acts as an independent learner to capture diverse, subtle forgery cues both granularly and holistically, while preserving the pretrained Vision-Language Alignment (VLA) knowledge, and ii) provides a complementary discriminative cue -- Identity-Aware VLA score, derived by coupling cross-modal semantics with the forgery cues learned by ForgePerceiver. Notably, the VLA score is augmented by an identity prior-informed text prompting to capture authenticity cues tailored to each identity, thereby enabling more discriminative cross-modal semantics. Comprehensive experiments on video DFD benchmarks, including classical face-swapping forgeries and recent full-face generation forgeries, demonstrate that our VLAForge substantially outperforms state-of-the-art methods at both frame and video levels. Code is available at https://github.com/mala-lab/VLAForge.
Jiawen Zhu, Yunqi Miao, Xueyi Zhang +2
Singapore Management University, Singapore · The University of Warwick, UK · Nanyang Technological University, Singapore +1
Current Vision-Language Models (VLMs) for deepfake detection excel at identifying spatial artifacts but overlook a critical dimension: temporal inconsistencies in video forgeries. Adapting VLMs to reason about these dynamic cues remains a distinct challenge. To bridge this gap, we propose Forensic Answer-Questioning (FAQ), a large-scale benchmark that formulates temporal deepfake analysis as a multiple-choice task. FAQ introduces a three-level hierarchy to progressively evaluate and equip VLMs with forensic capabilities: (1) Facial Perception, testing the ability to identify static visual artifacts; (2) Temporal Deepfake Grounding, requiring the localization of dynamic forgery artifacts across frames; and (3) Forensic Reasoning, challenging models to synthesize evidence for final authenticity verdicts. We evaluate a range of VLMs on FAQ and generate a corresponding instruction-tuning set, FAQ-IT. Extensive experiments show that models fine-tuned on FAQ-IT achieve advanced performance on both in-domain and cross-dataset detection benchmarks. Ablation studies further validate the impact of our key design choices, confirming that FAQ is the driving force behind the temporal reasoning capabilities of these VLMs.
Zheyuan Gu, Qingsong Zhao, Yusong Wang +6
Institute of Artificial Intelligence, China Telecom (TeleAI) · Peking University · Fudan University