Beyond Clean Benchmarks: A Reproducible Severity -Sweep Evaluation of AI Generated Image Detector Robustness

AI-generated image detectors are used often for verifying news, moderating content and handling evidence. However most tests for these detectors use images. Images that circulate in the world are often recompressed, resized, cropped or filtered. Large studies already show that detector accuracy drops when images are degraded. This study does not claim to discover that. Instead it offers a severity-sweep test. This test asks whether a detector that works well on images will still work well when the images are intentionally degraded. Two detectors that are publicly available were tested. CNNSpot is a network trained with ProGAN. UnivFD is a CLIP linear probe. The detectors were not retrained. They were tested on 2,000 images: 1,000 images and 1,000 synthetic images from Stable Diffusion v1.5, ADM, Midjourney and BigGAN. These images come from a community mirror of GenImage. Five image transformations were applied at levels of severity: JPEG compression, resizing, centre cropping, blur and Gaussian noise. Two chains of transformations were also used. On images UnivFD performed better than CNNSpot with AUROC values of 0.741 versus 0.662. All conditions showed the pattern. Gaussian blur was the transformation that consistently lowered AUROC for both detectors. For CNNSpot AUROC fell from 0.661 to 0.627 when sigma increased from 1 to 4. For UnivFD AUROC fell from 0.733 to 0.688 over the range. JPEG compression, resizing and noise produced non-monotonic changes. Cropping unexpectedly improved AUROC for both detectors. No single transformation reduced AUROC by than 0.10. The chains of transformations were usually no more harmful than the steps. Two of four planned detectors were tested. The dataset is small. Contains a mix of generators. Therefore the findings apply to these two detectors in these conditions. The results indicate that the reliability of detection tools should be checked step by step for each transformation than relying on a clean-image benchmark score. This has implications for how organisations validate, deploy and govern tools.