TADA adapts steganalysis models to unknown JPEG processing pipelines via data emulation from small unlabeled sets, yielding gains in robustness to cover source mismatch over baselines.
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A self-supervised Degradation Estimation Network estimates parameters for physics-informed noise distributions to generate realistic synthetic low-light data, showing gains on noise replication, enhancement, and detection tasks.
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Tackle CSM in JPEG Steganalysis with Data Adaptation
TADA adapts steganalysis models to unknown JPEG processing pipelines via data emulation from small unlabeled sets, yielding gains in robustness to cover source mismatch over baselines.
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Towards a General-Purpose Zero-Shot Synthetic Low-Light Image and Video Pipeline
A self-supervised Degradation Estimation Network estimates parameters for physics-informed noise distributions to generate realistic synthetic low-light data, showing gains on noise replication, enhancement, and detection tasks.