Towards Universal Fake Image Detectors that Generalize Across Generative Models (2302.10174v2)

Abstract: With generative models proliferating at a rapid rate, there is a growing need for general purpose fake image detectors. In this work, we first show that the existing paradigm, which consists of training a deep network for real-vs-fake classification, fails to detect fake images from newer breeds of generative models when trained to detect GAN fake images. Upon analysis, we find that the resulting classifier is asymmetrically tuned to detect patterns that make an image fake. The real class becomes a sink class holding anything that is not fake, including generated images from models not accessible during training. Building upon this discovery, we propose to perform real-vs-fake classification without learning; i.e., using a feature space not explicitly trained to distinguish real from fake images. We use nearest neighbor and linear probing as instantiations of this idea. When given access to the feature space of a large pretrained vision-LLM, the very simple baseline of nearest neighbor classification has surprisingly good generalization ability in detecting fake images from a wide variety of generative models; e.g., it improves upon the SoTA by +15.07 mAP and +25.90% acc when tested on unseen diffusion and autoregressive models.

Towards Universal Fake Image Detectors That Generalize Across Generative Models

The rapid evolution and proliferation of advanced generative models, such as GANs, diffusion models, and autoregressive models, pose significant challenges for the detection of fake images generated by these models. The paper "Towards Universal Fake Image Detectors that Generalize Across Generative Models" addresses the critical need for general-purpose fake image detectors capable of identifying fake images from emergent and unseen generative model types, with limited training data. The research identifies weaknesses in conventional deep network approaches and proposes alternative methodologies leveraging pre-trained models like CLIP-ViT, to achieve exceptional generalization capabilities.

Key Findings and Methodology

The prevailing approach to fake image detection involves training deep networks for binary classification between real and fake images. These models rely heavily on recognizing low-level artifacts specific to the generative models on which they were trained. However, this paper reveals that such networks often fail to detect fake images from unseen generative models due to their over-reliance on model-specific artifacts. When trained on GAN-generated data, for instance, these classifiers do not generalize well to images synthesized by diffusion or autoregressive models.

The paper introduces a novel detector design eschewing traditional learning-based classifiers in favor of methods based on feature spaces that are not specifically trained for the task at hand. By utilizing the CLIP-ViT model pre-trained on extensive internet-scale datasets and employing nearest neighbor (NN) and linear probing methods, the analysis demonstrates a substantial improvement in the ability to generalize across various generative models.

The results are compelling: the simple baseline of nearest neighbor classification using the CLIP-ViT feature space notably outperforms state-of-the-art methods by +15.07 mAP and +25.90% accuracy on generative models not seen during training, such as diffusion and autoregressive models. This robust performance underscores the effectiveness of using generalized feature spaces over custom learning for specific real-vs-fake distinctions.

Implications and Speculative Future Directions

The implications of this research are significant for the development of more resilient and universally applicable fake image detectors. By demonstrating the versatility of using large pre-trained models, this work highlights the potential for adapting existing AI models to novel applications beyond their original intent. This approach not only advances fake detection capabilities but also offers a sustainable path by potentially reducing computational costs associated with training specialized models.

Looking forward, research could explore further enhancing the robustness and efficiency of such detectors, even with limited data from new generative models. It may be beneficial also to investigate the feature characteristics within CLIP's space that facilitate such effective classification. Moreover, the broader application of this methodology in other forensic domains, such as deepfake video detection or audio synthesis, could unlock practical solutions to evolving digital content authenticity challenges.

Conclusion

In summary, the paper provides a compelling alternative to traditional real-vs-fake classifiers by emphasizing the utility of a powerful, pre-trained vision-LLM like CLIP-ViT. The results underscore the inadequacy of current model-specific classifiers in generalizing across generative model families, thus setting a firm foundation for future exploration in universal fake image detection systems. This work not only advances the technical frontier but also offers strategic insights into leveraging cross-domain, pre-trained model capabilities for broader applications, reflecting an important shift in AI-driven content verification strategies.