Does C3-B116-G42-02 overstate how well a ProGAN-trained detector holds up on GANs, and which training set do its Chameleon numbers belong to?
Problem. C3-B116-G42-02 says the ProGAN-trained real-vs-fake classifier 'keeps near-perfect accuracy on GANs' and gives UnivFD's Chameleon result as 74.97 percent of fakes caught and 41.09 percent of real images passed. Conditions. Two conditions were dropped. (1) Ojha et al. (arXiv 2302.10174v2) call the generalisation 'excellent' only within the ProGAN-to-StyleGAN family; the same paper's Table 1 shows the plain ProGAN-trained classifier averaging 80.77 (CycleGAN) and 79.25 (GauGAN), with fake-image accuracy of 62.91 and 59.1, and the text gives the best trained-network baseline as about 85 percent average on GANs, versus about 93 and 95 percent for the NN and linear-probe variants. So 'near-perfect' holds for the seen generator and close relatives, not for all GANs. (2) In the AIDE paper (arXiv 2406.19435v3) Table 5 the 74.97/41.09 pair is UnivFD trained on SD v1.4 images, given as 'fake image / real image' accuracy. In the ProGAN-trained row the same UnivFD column reads 3.18/97....
Structured fields
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"url": "https://arxiv.org/html/2302.10174v2",
"note": "Sec. 1 / Table 1 (flattened); Sec. 1 results paragraph; Table 5 caption; Table 5, SD v1.4 training row (flattened); Table 5, ProGAN training row (flattened)"
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"url": "https://arxiv.org/html/2406.19435v3",
"note": "Sec. 1 / Table 1 (flattened); Sec. 1 results paragraph; Table 5 caption; Table 5, SD v1.4 training row (flattened); Table 5, ProGAN training row (flattened)"
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- [sourced_fact] Problem. C3-B116-G42-02 says the ProGAN-trained real-vs-fake classifier 'keeps near-perfect accuracy on GANs' and gives UnivFD's Chameleon result as 74.97 percent of fakes caught and 41.09 percent of real images passed. Conditions. Two conditions were dropped. (1) Ojha et al. (arXiv 2302.10174v2) call the generalisation 'excellent' only within the ProGAN-to-StyleGAN family; the same paper's Table 1 shows the plain ProGAN-trained classifier averaging 80.77 (CycleGAN) and 79.25 (GauGAN), with fake-image accuracy of 62.91 and 59.1, and the text gives the best trained-network baseline as about 85 percent average on GANs, versus about 93 and 95 percent for the NN and linear-probe variants. So 'near-perfect' holds for the seen generator and close relatives, not for all GANs. (2) In the AIDE paper (arXiv 2406.19435v3) Table 5 the 74.97/41.09 pair is UnivFD trained on SD v1.4 images, given as 'fake image / real image' accuracy. In the ProGAN-trained row the same UnivFD column reads 3.18/97.83, i.e. almost every fake passed as real. Method. Re-read both tables with the column headers (the arXiv HTML flattens them; column order CycleGAN, GauGAN, LDM, Guided, DALL-E for Table 1; CNNSpot, FreDect, Fusing, GramNet, LNP, UnivFD, DIRE, PatchCraft, NPR, AIDE for Table 5) and always state the training set when quoting a detector score. Failure modes. Quoting 74.97/41.09 without the training set implies a general UnivFD result; it is one training configuration and it trades a 41 percent real-image pass rate for the catch rate. Limitations. Flattened-table reading; the ordering above was inferred from the table headers and from the arithmetic mean check on the AIGCDetect row (the 16 UnivFD values average 78.43). Version/date. arXiv 2302.10174v2 and 2406.19435v3, both the latest versions on 2026-10-23. · locator: Sec. 1 / Table 1 (flattened); Sec. 1 results paragraph; Table 5 caption; Table 5, SD v1.4 training row (flattened); Table 5, ProGAN training row (flattened) source
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| component | license | hosting | redistribution | modification | training | Evidence and notes |
|---|---|---|---|---|---|---|
| documentation | unknown | permitted | permitted | unknown | unknown | Applies to this original summary only, not to the cited sources. |