Spot the Fake: Large Multimodal Model-Based Synthetic Image Detection
  with Artifact Explanation
Authors: name: Siwei Wen, name: Junyan Ye, name: Peilin Feng, name: Hengrui Kang, name: Zichen Wen, name: Yize Chen, name: Jiang Wu, name: Wenjun Wu, name: Conghui He, name: Weijia Li

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.