A Unified Multi-Task Vision Transformer for AI-Generated Image Detection and Generator Attribution

The rapid advancement of diffusion-based generative models such as Stable Diffusion, DALL·E 3, and Midjourney has

significantly reduced the visual distinction between synthetic and real imagery. While most existing work focuses primarily on

binary AI-image detection, relatively limited attention has been given to unified detection and generator attribution within a

single framework. This paper presents a multi-task Vision Transformer (ViT) approach that jointly performs AI-generated image

detection and source attribution using a shared transformer backbone with task-specific classification heads. This multi-task

approach allows the model to understand general characteristics of AI-generated images while also recognizing patterns linked

to particular generators. To evaluate reliability under practical conditions, a robustness-aware evaluation protocol is introduced

that measures prediction variance and decision-flip rate under typical image transformations such as JPEG compression and

resizing. In addition, a gradient-free occlusion-based explainability method is integrated to support interpretable and CPUcompatible inference.

Experimental results demonstrate that the proposed framework achieves high detection reliability and strong attribution

performance while maintaining stable behaviour under common image transformations. The overall system provides a unified

and deployment-oriented approach for AI-image authenticity assessment in real-world environments.