Tackle CSM in JPEG Steganalysis with Data Adaptation
Steganalysis models excel on benchmark datasets but struggle in the wild when analyzed images are produced by a processing pipeline unseen during training. This problem known as Cover Source Mismatch (CSM) is particularly hard in realistic settings where practitioners (1) have access to only a small, unlabeled dataset, (2) are unsure of the processing techniques applied to these images, and (3) lack information on the proportion of covers and stegos in that set. To answer this challenge, we introduce TADA (Target Alignment through Data Adaptation), a framework learning to emulate the unknown processing pipeline from a small unlabeled target set. This architecture is trained with a loss co...
| Authors | Rony Abecidan, Vincent Itier, Jérémie Boulanger et al. |
|---|---|
| Primary category | eess.IV |
| Published | 19 May 2026 |
| Last updated at source | 19 May 2026 |
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