Learning a Semantic Similarity Orthogonal Space for Model‐Level <scp>AI</scp> ‐Generated Image Source Attribution

ABSTRACT The rapid evolution of diffusion models has established artificial intelligence generated image (AIGI) as a dominant paradigm in digital media, simultaneously escalating the risks of deepfakes and copyright infringement. While existing forensic methods focus primarily on distinguishing real from synthetic content, they fail to address the critical attribution challenge: identifying the specific model architecture responsible for a generated image. To bridge this gap, this paper proposes a novel AIGI source tracing framework capable of pinpointing source models in black‐box scenarios. Our approach is grounded in the hypothesis that images generated by the same model under identical semantic conditions exhibit consistent stylistic signatures and detail‐level artefacts. The framework operates through a ‘reconstruct‐and‐compare’ paradigm. First, we employ a CLIP‐based optimization algorithm to reconstruct the semantic prompt of the query image, enabling the generation of a reference sample from the candidate model. Second, to accurately measure the provenance similarity between the query and the reference, we introduce a Semantic Comparison Network based on Orthogonal Extension. This network utilizes a feature purification mechanism to decouple shared semantic content from model‐specific visual attributes. Furthermore, it employs a multi‐stage orthogonal training strategy to extract mutually independent feature subspaces, ensuring a comprehensive capture of diverse stylistic dimensions. Experimental results demonstrate that this framework effectively overcomes the limitations of current classifier‐based attribution, offering a robust solution for digital forensics and accountable AI governance.