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This study investigates the robustness of AI-art detectors when faced with generator shifts, specifically using a dataset from Stable Diffusion 3.5 Medium across ten art styles. The researchers found that while deep learning models excel in identifying art from the same generator family, they struggle significantly when confronted with images generated by different architectures, leading to a high rate of misclassification. The results underscore a critical generalization gap in current detectors, suggesting the need for more adaptable detection systems to keep pace with advancements in generative models.
AI-art detectors misclassify up to 40% of images from new generative models, revealing a dangerous vulnerability in copyright and authenticity verification.
Text-to-image generative models have advanced rapidly, with modern Diffusion Transformer architectures producing images that are increasingly difficult to distinguish from human-created artwork. This development has raised significant concerns regarding copyright protection, misinformation, fraud, impersonation, and the authenticity of digital content. Most AI-art detectors are trained and evaluated on the same generator family, leaving robustness to newer architectures underexplored. In this chapter, we analyze generator shift based on a Stable Diffusion 3.5 Medium (SD3.5m) artwork dataset spanning ten art styles through reverse prompting of held-out human artwork samples. Five detectors are trained on U-Net-based latent diffusion artwork and evaluated in a zero-shot cross-generator setting on the SD3.5m dataset. Deep learning models perform strongly in-distribution but degrade under generator shift, misclassifying many SD3.5m images as human while human false positives remain low. The CLIP ViT-L/14 model performs best overall, while Grad-CAM analysis reveals weaker and more diffuse activation on false negatives. These findings highlight a generalization gap in current AI-art detectors and motivate the development of detectors as one component of a layered defense that remains reliable across rapidly evolving generative architectures.
