ExDA: Towards Universal Detection and Plug-and-Play Attribution of AI-Generated Ex-Regulatory Images

As image-generative AI models become increasingly accessible to the public, the demand for content safety has surged. Although model developers have introduced alignment mechanisms to prevent the creation of threatening images, and extensive researches have been conducted on verifying the authenticity of AI-generated images, a significant number of ex-regulatory images have been discovered that fall into regulatory gaps. These images are neither covered by existing alignment mechanisms nor included in the scope of current detection methods. To address this, we introduce ExDA, a detection and attribution framework specifically designed for such ex-regulatory images. ExDA utilizes a frozen CLIP:ViT-L/14 as a visual feature extractor to extract rich and unbiased visual features, complemented by a text feature reduction layer to unify semantic styles. For obtaining highly discriminative features, ExDA introduces an SFS-ResNet network, where each basic layer is replaced with a meticulously designed Multi-Channel Margin Convolution (MMConv). Additionally, a plug-and-play multi-generation model attributor is integrated behind the detector. Given the lack of ex-regulatory images in existing public datasets, we constructed ExImage, a dataset containing 72,000 ex-regulatory images, to validate ExDA's effectiveness. Experiments show that ExDA achieves an average detection accuracy of 99.07% on ExImage, and demonstrating significant performance improvements of +5.73% and +10.36% on GenImage and high-challenge Chameleon datasets respectively in cross-datasets evaluation. Notably, ExDA also achieves excellent performance in attribution tasks, demonstrating its superior ability to identify the intrinsic fingerprints of generative models. Our code is available at https://github.com/mwp-create-wonders/ExDA.