Title: Spot the Fake: Large Multimodal Model-Based Synthetic Image Detection with Artifact Explanation
Venue: NeurIPS 2025
Accepted: yes
Keywords: Synthetic Image Detection

Deepfake

Large Multimodal Model
Abstract: With the rapid advancement of Artificial Intelligence Generated Content (AIGC) technologies, synthetic images have become increasingly prevalent in everyday life, posing new challenges for authenticity assessment and detection. Despite the effectiveness of existing methods in evaluating image authenticity and locating forgeries, these approaches often lack human interpretability and do not fully address the growing complexity of synthetic data. To tackle these challenges, we introduce FakeVLM, a specialized large multimodal model designed for both general synthetic image and DeepFake detection tasks. FakeVLM not only excels in distinguishing real from fake images but also provides clear, natural language explanations for image artifacts, enhancing interpretability. Additionally, we present FakeClue, a comprehensive dataset containing over 100,000 images across seven categories, annotated with fine-grained artifact clues in natural language. FakeVLM demonstrates performance comparable to expert models while eliminating the need for additional classifiers, making it a robust solution for synthetic data detection. Extensive evaluations across multiple datasets confirm the superiority of FakeVLM in both authenticity classification and artifact explanation tasks, setting a new benchmark for synthetic image detection. The code, model weights, and dataset can be found here: https://github.com/opendatalab/FakeVLM.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper addresses the increasingly pressing challenge of detecting AI-generated images by introducing FakeVLM, a large multimodal model (LMM) that not only distinguishes between real and synthetic images with high accuracy but also explains the detected artifacts in natural language, thereby markedly improving interpretability. To train and evaluate the model, the authors construct FakeClue, a large-scale, multi-category dataset of more than 100 000 images. FakeClue is annotated via an innovative multi-LMM pipeline that generates fine-grained textual descriptions of synthetic artifacts. Experiments show that FakeVLM surpasses general-purpose LMMs and rivals task-specific expert models across multiple benchmarks, establishing a strong and interpretable baseline for synthetic-image forensics.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths\n\n1. The paper tackles the important problem of enhancing the interpretability of synthetic-image detection and articulates a clear research motivation and significance.\n\n2. The proposed method is simple yet extensible: it employs LMMs to construct an automatic annotation pipeline, enabling the creation of a large, diverse dataset.\n\n3. Extensive experiments demonstrate that the approach is competitive with both expert models and general-purpose LMMs, and a broad set of comparisons is provided.\n\nWeaknesses\n\n1. The current presentation of the FakeClue dataset lacks comprehensive visual exemplars that cover the entire spectrum of image types.\n\n2. Most experiments appear to be conducted on relatively idealized datasets, raising concerns about FakeVLM’s robustness under image degradation or other real-world distortions.\n\n3. FakeClue spans seven categories (animals, humans, scenes, satellites, documents, etc.). It is unclear whether FakeVLM performs uniformly well across all categories; category-specific biases might compromise the model’s general applicability.\n\nQUESTIONS:\nSee weaknesses.\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 6, "REVIEWER_CONFIDENCE": 5, "CLARITY": 4, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-29"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper introduces the FakeClue dataset, comprising 100K images with fine-grained artifact annotations, designed to facilitate explanation of deepfake detection using multimodal large language models (MLLMs). Based on this dataset, the authors fine-tune LLaVA-1.5-7B and conduct a series of experiments to demonstrate its effectiveness.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths\n1. The construction pipeline of FakeClue is well-defined and task-oriented. Integrating category-specific knowledge into prompt engineering is a good insight. And Section 3 would benefit from including additional category-specific annotation examples.\n2. The exploration of MLLMs for deepfake detection is insightful. Section 4.1 shows that using frozen MLLM features with a classification head outperforms a simple Real/Fake question-answering setting. This highlights the inherent difficulty of aligning complex visual content with binary responses, and supports the rationale for leveraging MLLMs for explanation-based detection.\n\nWeaknesses\n1. The quality of annotations generated by open-source MLLMs is questionable. Prior works such as FakeBench have demonstrated that open-source models lag significantly behind commercial LMMs (e.g., GPT-4o) in interpretability. The paper should include further visualization or discussion to support that the aggregated annotations from open-source MLLMs can achieve comparable quality to those from stronger proprietary models.\n2. The methodological novelty of the paper is limited. Section 4 largely reiterates the architecture of existing LLaVA models. While explaining deepfake detection via MLLMs is a promising direction, simply fine-tuning existing architectures on a new dataset is insufficient. The paper would be significantly strengthened by introducing task-specific modules or architectural innovations.\n3. The experimental section requires improvement. First, the set of expert baselines in Table 2 is outdated and limited. Important recent methods such as AIDE (ICLR 2025), HEIE (CVPR 2025), and C2P (AAAI 2024) should be included for a fair comparison. Second, the inclusion of FWA and CDFA in Table 5 seems irrelevant to deepfake detection—was this a mistake? Lastly, to justify the value of explanation-based tasks, the paper should compare simple Real/Fake QA against explanatory QA. \n4. The category design in FakeClue is problematic. It is unclear why “human” and “deepfake” are treated as separate categories. As an academic term, “deepfake” should not serve as a categorical label. Moreover, the appendix reveals that prompts provided for “human”, with no corresponding prompts for “deepfakes”.\n\nQUESTIONS:\nPlease see weakness\n\nLIMITATIONS:\nPlease see weakness", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 5, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 2, "DATE": "2025-07-01"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes FakeVLM, a fully fine-tuned large multimodal model based on LLaVA-1.5-7B. By pairing a CLIP-ViT visual encoder with a Vicuna language head, the model judges the authenticity of images and provides natural-language explanations of visual artifacts. The authors also construct the FakeClue dataset, which covers seven categories—animals, humans, scenes, satellite images, and others—and contains over 100,000 real and synthetic images. Fine-grained artifact descriptions are generated through multi-model collaborative annotation and rule-based voting. Experiments show that FakeVLM outperforms strong baselines such as GPT-4o and Qwen2-VL on benchmarks including FakeClue, LOKI, DD-VQA, and DMImage, and remains robust under common distortions such as JPEG compression, resizing, and Gaussian noise. The released model weights and dataset provide a unified and interpretable framework for synthetic-image detection.\n\nSTRENGTHS AND WEAKNESSES:\n**Strengths:**\n1. This work addresses an important interpretability issue in synthetic-image detection with a simple yet effective approach, enhancing explainability.\n2. The authors release FakeClue, a new dataset with more than 100,000 annotated samples from diverse domains.\n3. The method is compared in detail across multiple datasets and models.\n4. Experimental results show that the approach is highly competitive, outperforming large general-purpose LMMs and several domain-specific expert models.\n\n**Weaknesses:**\n1. Only mild JPEG compression, resizing, and Gaussian noise are tested; additional common perturbations should be included to fully assess model stability.\n2. The process of deriving final annotations from the outputs of three models is insufficiently detailed; more specifics are needed.\n3. The paper lacks a description of how the category-specific annotation prompts were obtained.\n\nQUESTIONS:\n1. What are the main differences in the annotation prompts for different categories, and how were they designed?\n2. How are the outputs of the three models aggregated? What happens when their predictions differ?\n3. Clarifying whether different categories focus on different types of artifacts and what the main differences are would also help understanding.\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-02"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis study is focused on creating a diverse dataset, namely FakeClue, and use the new dataset to train a new generalized detector both synthetic and deepfake images. The new dataset is derived from a number of published datasets, like FF++, GenImage, CVUSE, ViGOR, etc with additional category labels in 7 categories, and captions that are generated by fusing Qwen2-VL, InterVL, DeepSeek captions that explain the justification of real/fake labels.  While it is generally risky to use ground truth that is strictly based on ML models which were not trained on the specific task, Section 4.1 is a good ablation study to establish that the FakeClue labels align well with human labels after the explanations. \n\nThe diversity of the dataset is particularly valuable to avoid overfitting to smaller datasets for simpler tasks, like only detecting fake faces. The model, which is based on the existing LLAVA 1.5, is trained on FakeClue seems to match or outperform the benchmarks models while being tested on the other public datasets. It is intuitive to expect that with more data, and more instructive labels with categories and explanations, the trained model performs better.\n\nOverall, this is a solid dataset paper;  the study systematically constructs a diverse datasets with categories, fake/real labels, and human understandable explanations for fake/real labels. The results show that FakeVLM model trained by the new dataset outperforms the existing SOTA models for synthetic image detection. The DeepFake detection results on FF++ also look impressive.\n\nSTRENGTHS AND WEAKNESSES:\nQuality:\nThe work follows a systematic approach to generate a large and diverse dataset, namely FakeClue. The new dataset is used to train a new model, FakeVLM. The ablation studies are clear and well motivated. The results show significant improvements over the existing benchmarks. Overall, this is a solid dataset paper.\n\nClarity:\nThe paper is well written and easy to follow.  The results with the existing datasets and the new dataset allow reproduction of results. One typo in 278, DMimage dataset name seems misspelled.\n\nSignificance:\nI believe the new dataset allows all fake/real classification models to improve; this is a significant dataset contribution.\n\nOriginality:\nThis is a dataset paper; the FakeClue dataset combining a variety of artifacts, categories, multi-LMM labels of fake/real and explanations is a new dataset as explained in Table 1.\n\nQUESTIONS:\n1. The FakeClue dataset generates its labels combining the three major pre-trained models, Qwen2-VL, InterVL, and DeepSeek. If there are synthetic images in the dataset that are not detectable by all three, the dataset will contain incorrect labels. A detector trained by this dataset will learn to label such synthetic images as real and have a possibly bigger gap in detecting such images. How would the authors think about generalizing beyond the capabilities of these major pre-trained models?  \n\n2. An adversarial attacker may use the same three models to figure out the blind spots for all models that are trained with FakeClue. How would authors think about closing such gaps?\n\n3. The DeepFake detection results for all models are a bit dubious because most models are performing over 95% on FF++ dataset; this indicates a need for a more challenging benchmark for DeepFake detection. Evaluations with CDFv2 or DFDCP may reveal true performance differences, if there are any.\n\nCDFv2: Li , Y., Yang , X., Sun , P., Qi 370 , H., & Lyu , S. (CVPR 2020) Celeb-df: A new dataset for deepfake forensics\n\nDFDCP: Dolhansky , B., Bitton , J., Pflaum , B., Lu , J., Howes , R., Wang , M., & Ferrer , C. C. (Arxiv 2020) The deepfake detection challenge dataset\n\nLIMITATIONS:\nDetecting fake images have very important applications in real life, as explained in the paper introduction. The paper contributes a valuable dataset that can be used to improve all synthetic and deep fake detection methods.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 3, "CLARITY": 4, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-02"}