基于集成学习的AI生成图像对抗检测框架OA

[Objective]With the rapid development of generative adversarial networks(GANs)and the generative diffusion models,the quality of AI-generated images has been continuously im-proved reaching to the point where it is challenging for the human eye to distinguish the AI-gen-erated 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 evi-dence 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 detec-tion methods into an adversarial detection framework.It uses a random forest model to combine the output of in-dividual 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 GenIm-age 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 de-tection and attribution research..