Spot the Fake: Large Multimodal Model-Based Synthetic Image Detection
  with Artifact Explanation
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.
67e250450487eeecfd9a5880
Siwei Wen
67e250450487eeecfd9a5881
Junyan Ye
67e250450487eeecfd9a5882
Peilin Feng
67e250450487eeecfd9a5883
Hengrui Kang
67e250450487eeecfd9a5884
Zichen Wen
claimed_verified
2025-03-27T09:05:04.610Z
653b8c3e97a4d71d950e2f20
/avatars/b68880022e14556d0be58c69615db3be.svg
Zichen Wen
user
zichenwen
67e250450487eeecfd9a5885
Yize Chen
67e250450487eeecfd9a5886
Jiang Wu
67e250450487eeecfd9a5887
Wenjun Wu
67e250450487eeecfd9a5888
Conghui He
67e250450487eeecfd9a5889
Weijia Li
Spot the Fake: Large Multimodal Model-Based Synthetic Image Detection
  with Artifact Explanation
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.
FakeVLM, a specialized multimodal model, enhances synthetic image and DeepFake detection with human-interpretable explanations.
multimodal model DeepFake image artifacts natural language explanations authenticity classification