Unknown Aware AI-Generated Content Attribution

TL;DR

This paper addresses the challenge of attributing AI-generated images to specific models by leveraging limited labeled data and unlabeled wild data, improving identification of unseen generators in open-world scenarios.

Contribution

It introduces a constrained optimization method that effectively uses unlabeled wild data to enhance attribution accuracy for unseen generative models.

Findings

Wild data improves attribution to unseen generators

Baseline struggles with new, unseen models

Proposed method outperforms traditional classifiers

Abstract

The rapid advancement of photorealistic generative models has made it increasingly important to attribute the origin of synthetic content, moving beyond binary real or fake detection toward identifying the specific model that produced a given image. We study the problem of distinguishing outputs from a target generative model (e.g., OpenAI Dalle 3) from other sources, including real images and images generated by a wide range of alternative models. Using CLIP features and a simple linear classifier, shown to be effective in prior work, we establish a strong baseline for target generator attribution using only limited labeled data from the target model and a small number of known generators. However, this baseline struggles to generalize to harder, unseen, and newly released generators. To address this limitation, we propose a constrained optimization approach that leverages unlabeled wild data, consisting of images collected from the Internet that may include real images, outputs from unknown generators, or even samples from the target model itself. The proposed method encourages wild samples to be classified as non target while explicitly constraining performance on labeled data to remain high. Experimental results show that incorporating wild data substantially improves attribution performance on challenging unseen generators, demonstrating that unlabeled data from the wild can be effectively exploited to enhance AI generated content attribution in open world settings.