  With the rapid development of deep generative models (such as Generative
Adversarial Networks and Diffusion models), AI-synthesized images are now of
such high quality that humans can hardly distinguish them from pristine ones.
Although existing detection methods have shown high performance in specific
evaluation settings, e.g., on images from seen models or on images without
real-world post-processing, they tend to suffer serious performance degradation
in real-world scenarios where testing images can be generated by more powerful
generation models or combined with various post-processing operations. To
address this issue, we propose a Global and Local Feature Fusion (GLFF)
framework to learn rich and discriminative representations by combining
multi-scale global features from the whole image with refined local features
from informative patches for AI synthesized image detection. GLFF fuses
information from two branches: the global branch to extract multi-scale
semantic features and the local branch to select informative patches for
detailed local artifacts extraction. Due to the lack of a synthesized image
dataset simulating real-world applications for evaluation, we further create a
challenging fake image dataset, named DeepFakeFaceForensics (DF 3 ), which
contains 6 state-of-the-art generation models and a variety of post-processing
techniques to approach the real-world scenarios. Experimental results
demonstrate the superiority of our method to the state-of-the-art methods on
the proposed DF 3 dataset and three other open-source datasets.
