IBRSteG learns a scene-independent embedding function via the GAS network to hide secret 3DGS scenes inside cover scenes with high visual quality, capacity, and security.
StegExpose - A Tool for Detecting LSB Steganography
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
abstract
Steganalysis tools play an important part in saving time and providing new angles of attack for forensic analysts. StegExpose is a solution designed for use in the real world, and is able to analyse images for LSB steganography in bulk using proven attacks in a time efficient manner. When steganalytic methods are combined intelligently, they are able generate even more accurate results. This is the prime focus of StegExpose.
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cs.CV 3verdicts
UNVERDICTED 3representative citing papers
Proposes a prompt-free latent diffusion steganography method using style semantics, CACM for reversible mapping, and predictor-corrector sampling to improve stego image quality and secret recovery.
Introduces binary attention mechanism to image steganography for higher payload capacity with reduced feature map distortion and resistance to steganalysis.
citing papers explorer
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IBRSteG: Learning a Generalizable Steganography Framework for 3D Gaussian Splatting
IBRSteG learns a scene-independent embedding function via the GAS network to hide secret 3DGS scenes inside cover scenes with high visual quality, capacity, and security.
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No Prompt, No Leaks: A Robust Generative Steganography Framework via Prompt-Free Diffusion
Proposes a prompt-free latent diffusion steganography method using style semantics, CACM for reversible mapping, and predictor-corrector sampling to improve stego image quality and secret recovery.
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BASN -- Learning Steganography with Binary Attention Mechanism
Introduces binary attention mechanism to image steganography for higher payload capacity with reduced feature map distortion and resistance to steganalysis.