Research on adversarial identification methods for AI-generated image software Craiyon V3.

Abstract

With the rapid development of diffusion models, AI generation technology can now generate very realistic images. If such AI-generated images are used as evidence, they may threaten judicial fairness. Taking the adversarial identification of images generated by Craiyon V3 software as an example, this paper studies the adversarial identification methods for AI-generated image software. First, an AI-generated image set containing 18,000 images is constructed using Craiyon V3; then, an AI-generated image detection model based on deep learning is selected, and a score-based likelihood ratio method is introduced to evaluate the strength of evidence. Experimental results show that the proposed method achieves an accuracy of over 99% on multiple threshold classifiers including Swin-Transformer, ResNet-18, and so on, and the fitted likelihood ratio model also passes a series of validation criteria including Tippett plots. The research results of this paper are expected to be applied to judicial practice in the future, providing judges with a reliable and powerful decision-making basis, and laying a foundation for further exploration of AI-generated image identification methods.