CoDA: Color Distribution Probing for Efficient and Generalizable AI-Generated Image Detection
Provides an efficient and robust solution for detecting AI-generated images across diverse domains and generators, addressing a gap in existing benchmarks.
AI-generated image detection faces a trade-off between generalization and efficiency. The authors introduce FakeForm, a large-scale benchmark for cross-model and cross-domain evaluation, and propose CoDA, a compact 1.48M-parameter detector using color-distribution probing. CoDA achieves state-of-the-art performance on standard benchmarks and best results on cross-domain evaluation.
AI-generated image detection faces a persistent trade-off between generalization and efficiency: lightweight artifact-based methods often degrade on unseen generators or domains, whereas more robust large-scale models are computationally expensive. Meanwhile, existing benchmarks mainly focus on cross-model evaluation in photorealistic settings, leaving cross-domain robustness underexplored. To address this gap, we introduce FakeForm, a large-scale benchmark with approximately 370,000 images across 62 diverse domains for both cross-model and cross-domain evaluation. Motivated by this broader setting, we revisit color-distribution probing as an efficient complementary cue for AI-generated image detection. We observe that, especially for photographic content, real photographs tend to exhibit smoother and more stable color patterns, whereas synthetic images often show characteristic color imbalances introduced by neural generation. Based on this observation, we propose CoDA, a compact 1.48M-parameter detector built on a Noise-Quantization Probe, together with a theoretical analysis linking probe responses to color non-uniformity. Experiments show that CoDA achieves state-of-the-art performance on standard benchmarks and the best results on the challenging cross-domain evaluation of FakeForm, while remaining highly competitive in cross-model photorealistic settings. These results suggest that persistent generative artifacts can provide a practical foundation for efficient and robust AI-generated image detection. The models and FakeForm benchmark will be made publicly available.