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.
Title resolution pending
4 Pith papers cite this work. Polarity classification is still indexing.
4
Pith papers citing it
representative citing papers
A Bayesian optimal experimental design framework with Gaussian approximation of expected information gain and surrogate Fisher information enables optimized uniaxial tests that significantly improve identifiability of history-dependent constitutive parameters over random designs.
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.
citing papers explorer
-
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.
-
Optimal Experimental Design for Reliable Learning of History-Dependent Constitutive Laws
A Bayesian optimal experimental design framework with Gaussian approximation of expected information gain and surrogate Fisher information enables optimized uniaxial tests that significantly improve identifiability of history-dependent constitutive parameters over random designs.
-
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.
- PixIE: Prompted Pixel-Space Low-Light Image Enhancement