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A flag is not an explanation. It means the image carried a statistical trace that resembled the generated pictures a detector was trained on, scored past a threshold, and nothing more. It does not tell you which part of the image moved the decision, and the research keeps landing on an uncomfortable point: the trace a detector reads comes from how generators are built, and an ordinary camera or editing pipeline can leave patterns that look almost the same.
What does a “flag” actually mean?
A detector outputs a score for how closely an image matches the traces it learned to associate with generation, and a threshold turns that score into a label; how a detector arrives at that score is set out in how AI image detectors actually work. The traces are real. Frank, Eisenhofer, Schönherr et al. (ICML 2020) showed that generated images carry periodic grid-like peaks in the frequency domain, strong enough that a simple classifier reached 100 percent test accuracy separating real faces from StyleGAN faces. But a flag built on that kind of signal tells you only that the image looked synthetic to one model at one threshold. It does not tell you the model was right, or what part of the file it read to get there.
Is the detector reading the picture, or the processing?
This is the heart of a false flag. The trace most detectors key on is a byproduct of generation, not a visible property of the image. Frank and colleagues tied the frequency artifacts to upsampling, “operations found in all current GAN architectures,” the step where a model scales a small feature map up to a full picture. The problem is that real-world pipelines also resample, sharpen, and compress, and those operations leave periodic patterns of their own. When a genuine photo’s processing happens to resemble the generation trace, the detector fires.
Worse, detectors often learn the wrong thing at training time. Grommelt, Weiss, Pfreundt et al. (2024) found that standard detection datasets carry “biases related to JPEG compression and image size,” and that detectors “indeed learn from these undesired factors.” Strip those biases and cross-generator accuracy moved by more than 11 percentage points, which means a large part of the original score was reading compression and resolution rather than whether the image was generated. A flag from a detector like that may be reading the container, not the content, the mechanism that produces AI image detector false positives.
Can compression alone trip the flag?
It can. Corvi, Cozzolino, Zingarini et al. (ICASSP 2023) noted that detectors “rely on traces that are invisible to the human eye,” and those traces sit in exactly the fine detail that compression rewrites. A real image that was re-saved, downsized, or double-compressed can pick up periodic patterns a narrow detector associates with its synthetic training set. The forensic side confirms how strong a signal compression history alone is: Kwon, Nam, Yu et al. (IJCV 2022) found that a standard RGB-pixel network could not even learn a double-JPEG task, collapsing to a 54.08 percent baseline that labeled nearly every image the same way, while a compression-aware model could read the history a second encoding leaves behind. Compression is its own loud signal, and a detector that was not built to separate it from generation can misread one as the other.
What would a trustworthy flag be reading?
The useful contrast is a detector grounded in the generator’s fingerprint rather than the file’s history. Ojha, Li, Lee (CVPR 2023) built a detector on the feature space of a large vision-language model and reached 94.19 mean average precision across 17 generators, far above a pixel baseline, because a broadly pretrained model has seen enough of the world to notice when something is off. Tan, Liu, Zhao et al. (CVPR 2024) read only the relationship between neighboring pixels that upsampling disturbs, which is closer to the generation trace and further from the compression trace. A flag resting on that kind of signal is worth more than one that could be reading a re-save, though even the best of them fall on a generator they never saw, as the next section shows.
Why do two detectors flag different images?
Because they read different traces and trained on different generators, so they fire on different pictures. Run several on the same set and the disagreement is stark. NewsGuard (2024) tested five detectors on 45 images and found at least one tool dissenting on 35 of them. Bellingcat (2023), testing the detector “AI or Not,” found it flagged 6 of 20 genuine contest photographs as AI, and separately called 7 of 10 compressed Midjourney images “real.” A flag is one narrow reader’s opinion, and a different reader with a different training set would draw the line somewhere else. Why that happens in detail is covered in why do AI detectors give different results.
So how should you treat a flag?
As a prompt to look closer, not a finding. Ask what the detector could have keyed on besides generation: a heavy re-save, a downsize to platform dimensions, an unusual camera or subject it has no reference for. Test the original file rather than a re-compressed copy, since the extra compression is often what pushes a real image over the line, a failure mode covered in can AI images be detected after compression. Run a second tool that reads a different signal, and weigh evidence off the pixels, such as the original source or the photographer’s own record, which outranks any score. A flag says “this resembled my generated examples.” Turning that into “a machine made this” is your job, not the detector’s. Putting that judgment to work on one image is the practical question in is this image AI-generated.
Sources
- Frank, Eisenhofer, Schönherr et al. (2020). Leveraging Frequency Analysis for Deep Fake Image Recognition. ICML 2020.
- Grommelt, Weiss, Pfreundt et al. (2024). Fake or JPEG? Revealing Common Biases in Generated Image Detection Datasets.
- Corvi, Cozzolino, Zingarini et al. (2023). On the Detection of Synthetic Images Generated by Diffusion Models. ICASSP 2023.
- Kwon, Nam, Yu et al. (2022). Learning JPEG Compression Artifacts for Image Manipulation Detection and Localization. IJCV 2022.
- Ojha, Li, Lee (2023). Towards Universal Fake Image Detectors that Generalize Across Generative Models. CVPR 2023.
- Tan, Liu, Zhao et al. (2024). Rethinking the Up-Sampling Operations in CNN-based Generative Network for Generalizable Deepfake Detection. CVPR 2024.
- NewsGuard (2024). Leading AI Image Detection Tools Mislead Online Users, Often Declaring Authentic Content Fake.
- Bellingcat (2023). Testing ‘AI or Not’: How Well Does an AI Image Detector Do Its Job?