title: Spot the Fake: Large Multimodal Model-Based Synthetic Image Detection
    with Artifact Explanation
summary: 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 dataset and code will be released in:
  https://github.com/opendatalab/FakeVLM.
paper_authors._id: 67e250450487eeecfd9a5880
paper_authors.name: Siwei Wen
paper_authors._id: 67e250450487eeecfd9a5881
paper_authors.name: Junyan Ye
paper_authors._id: 67e250450487eeecfd9a5882
paper_authors.name: Peilin Feng
paper_authors._id: 67e250450487eeecfd9a5883
paper_authors.name: Hengrui Kang
paper_authors._id: 67e250450487eeecfd9a5884
paper_authors.name: Zichen Wen
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-03-27T09:05:04.610Z
paper_authors._id: 653b8c3e97a4d71d950e2f20
paper_authors.avatarUrl: /avatars/b68880022e14556d0be58c69615db3be.svg
paper_authors.fullname: Zichen Wen
paper_authors.type: user
paper_authors.user: zichenwen
paper_authors._id: 67e250450487eeecfd9a5885
paper_authors.name: Yize Chen
paper_authors._id: 67e250450487eeecfd9a5886
paper_authors.name: Jiang Wu
paper_authors._id: 67e250450487eeecfd9a5887
paper_authors.name: Wenjun Wu
paper_authors._id: 67e250450487eeecfd9a5888
paper_authors.name: Conghui He
paper_authors._id: 67e250450487eeecfd9a5889
paper_authors.name: Weijia Li
paper_title: Spot the Fake: Large Multimodal Model-Based Synthetic Image Detection
    with Artifact Explanation
paper_summary: 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 dataset and code will be released in:
  https://github.com/opendatalab/FakeVLM.
paper_ai_summary: FakeVLM, a specialized multimodal model, enhances synthetic image and DeepFake detection with human-interpretable explanations.
paper_ai_keywords: multimodal model DeepFake image artifacts natural language explanations authenticity classification