pith:WIIXMMCX
Degradation-Consistent Paired Training for Robust AI-Generated Image Detection
Degradation-Consistent Paired Training raises AI-generated image detector accuracy on corrupted inputs by 9.1 percentage points with only a 0.9 percent drop on clean images.
arxiv:2604.10102 v2 · 2026-04-11 · cs.CV · cs.AI
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Claims
Experiments on the Synthbuster benchmark (9 generators, 8 degradation conditions) demonstrate that DCPT improves the degraded-condition average accuracy by 9.1 percentage points compared to an identical baseline without paired training, while sacrificing only 0.9% clean accuracy. The improvement is most pronounced under JPEG compression (+15.7% to +17.9%).
That the specific degradations (JPEG compression, Gaussian blur, resolution downsampling) and consistency losses used during training will generalize to unseen real-world corruptions and that the Synthbuster benchmark sufficiently represents practical deployment conditions.
DCPT raises average accuracy of AI image detectors under real-world degradations by 9.1 points on the Synthbuster benchmark using paired consistency losses, with only 0.9% drop on clean images and no added parameters or inference cost.
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Receipt and verification
| First computed | 2026-05-27T02:05:19.524692Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
b211763057642011c6557e273801b6f9ebfe7c12bb673b2c5296a6c87919ff0c
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curl -sH 'Accept: application/ld+json' https://pith.science/pith/WIIXMMCXMQQBDRSVPYTTQANW7H \
| jq -c '.canonical_record' \
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Canonical record JSON
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