Yang, M., Goenawan, G. J., Wang, H., Qin, H., Xu, C., Yang, Y., Fang, F., Sun, Y., Lim, J. H., & Zhu, H. (2026). Your AI-Generated Image Detector Can Secretly Achieve SOTA Accuracy, If Calibrated. Proceedings of the AAAI Conference on Artificial Intelligence, 40(14), 11622–11630. https://doi.org/10.1609/aaai.v40i14.38146
Abstract:
Despite being trained on balanced datasets, existing AI-generated image detectors often exhibit systematic bias at test time, frequently misclassifying fake images as real. We hypothesize that this behavior stems from distributional shift in fake samples and implicit priors learned during training. Specifically, models tend to overfit to superficial artifacts that do not generalize well across different generation methods, leading to a misaligned decision threshold when faced with test-time distribution shift. To address this, we propose a theoretically grounded post-hoc calibration framework based on Bayesian decision theory. In particular, we introduce a learnable scalar correction to the model’s logits, optimized on a small validation set from the target distribution while keeping the backbone frozen. This parametric adjustment compensates for distributional shift in model output, realigning the decision boundary even without requiring ground-truth labels. Experiments on challenging benchmarks show that our approach significantly improves robustness without retraining, offering a lightweight and principled solution for reliable and adaptive AI-generated image detection in the open world.
License type:
Publisher Copyright
Funding Info:
This research / project is supported by the A*STAR - Manufacturing, Trade, and Connectivity Programmatic Fund
Grant Reference no. : M23L7b0021
This research / project is supported by the National Natural Science Foundation of China - NA
Grant Reference no. : 62571412