Abstract:

[Objective] With the rapid development of generative adversarial networks (GANs) and the generative diffusion models, the quality of AI-generated images has been continuously improved reaching to the point where it is challenging for the human eye to distinguish the AI-generated images from real images. This technology has been commercialized, allowing users to generate images from text with one-click software products, creating certain commercial value. However, they also pose challenges to forensic identification. Using images as a direct evidence is undoubtedly an important research topic in forensic science. Therefore, detecting AI-generated images has become a critical issue that needs to be addressed. [Methods] Existing methods for detecting AI-generated images mainly focus on detecting images from a single generative model. However, the one-to-one detection method is poor in generalization capabilities when facing unseen generative models. This paper proposes a Stacking-based ensemble learning strategy that integrates various one-to-one detection methods into an adversarial detection framework. It uses a random forest model to combine the output of individual detectors specifically trained for different AI-generated image software, instead of direct generalization. [Results] Experimental results show that the framework achieved an overall accuracy of 98.36% on the GenImage dataset and demonstrated strong robustness on the artificial dataset designed in this study. The scores from the single detector outputs are retained, providing possibilities for subsequent attribution work. [Conclusions] The adversarial detection framework is promising to be used as a platform that can flexibly integrate and update various detection technologies, providing a more comprehensive and effective solution for AI-generated image detection and attribution research..

Key words: AI-generated images detection, ensemble learning, image detection framework