How well are open sourced AI-generated image detection models out-of-the-box: A comprehensive benchmark study
As AI-generated images proliferate across digital platforms, reliable detection methods have become critical for combating misinformation and maintaining content authenticity. While numerous deepfake detection methods have been proposed, existing benchmarks predominantly evaluate fine-tuned models, leaving a critical gap in understanding out-of-the-box performance -- the most common deployment scenario for practitioners. We present the first comprehensive zero-shot evaluation of 16 state-of-the-art detection methods, comprising 23 pretrained detector variants (due to multiple released versions of certain detectors), across 12 diverse datasets, comprising 2.6~million image samples spanning...
| Authors | Simiao Ren, Yuchen Zhou, Xingyu Shen et al. |
|---|---|
| Primary category | cs.CV |
| Published | 8 February 2026 |
| Last updated at source | 8 February 2026 |
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