SFIA: Toward a Generalized Semantic-Agnostic Method for Fake Image Attribution

Abstract

The proliferation of photorealistic images synthesized by generative adversarial networks (GANs) has posed serious threats to society. Therefore a new challenge task, named image attribution, is arising to attribute fake images to a specific GAN. However, existing approaches focus on model-specific features but neglect the misguidance of semantic-relevant features in image attribution, which leads to a significant performance decrease in cross-dataset evaluation. To tackle the above problem, we propose a semantic-agnostic fake image attribution (SFIA) method, which effectively distinguishes fake images by disentangling the GANs fingerprint and semantic-relevant features in latent space. Specifically, we design a semantic eliminator based on residual block with skip connections that take images as input and outputs GAN fingerprint features. A classifier with an attention module for feature refinement is introduced to make the final decision. In addition, we develop a well-trained reconstructor and classifier which supervise the semantic eliminator to achieve semantic-agnostic feature extraction. Moreover, we propose an improved data augmentation combined with meta-learning to enhance the model’s generalization in detecting unseen image categories. Comprehensive experiments on various datasets, namely, CelebA, LSUN-church, and LSUN-bedroom, demonstrate the effectiveness of our proposed SFIA. It achieves over 95% accuracy on three datasets and exhibits superior performance in terms of generalization to unseen data.