Assessing GAPL on completely unknown future generative models

Determine whether AI-generated images produced by next-generation generative models employing fundamentally new techniques still exhibit detectable artifacts, and rigorously assess the performance of the Generator-Aware Prototype Learning (GAPL) detector when applied to such completely unknown domains to ascertain whether these images remain detectable or become undetectable.

Background

The paper introduces Generator-Aware Prototype Learning (GAPL) to address the "Benefit then Conflict" dilemma in scaling AI-generated image (AIGI) detection across diverse generators. While GAPL achieves state-of-the-art results across six benchmarks, the authors note that these datasets still reflect known generative families (GANs, diffusion, and commercial APIs).

In the Limitation section, the authors explicitly acknowledge an unresolved question regarding generalization to completely unknown domains arising from fundamentally new generation techniques. They highlight uncertainty both about the presence of detectable artifacts in future models and about the detector’s performance in such scenarios, framing this as a problem for future research.