Results 61 to 70 of about 6,801,187 (249)

Rich model for Steganalysis of color images [PDF]

2014 IEEE International Workshop on Information Forensics and Security (WIFS), 2014
In this paper, we propose an extension of the spatial rich model for steganalysis of color images. The additional features are formed by three- dimensional co-occurrences of residuals computed from all three color channels and their role is to capture dependencies across color channels. These CRMQ1 (color rich model) features are extremely powerful for
Goljan, Miroslav, Fridrich, Jessica, Cogranne, Rémi   +2 more
openaire   +2 more sources

Features-pooling blind jpeg image steganalysis

, 2008
In this research, we introduce a new blind steganalysis in detecting grayscale JPEG images. Features-pooling method is employed to extract the steganalytic features and the classification is done by using neural network.
Leng, Chiew Kang, Josef Pieprzyk, Chiew Kang Leng, Pieprzyk, Josef   +3 more
core   +1 more source

Evaluation of Deep Learning and Conventional Approaches for Image Steganalysis [PDF]

, 2020
Steganography is the technique that's used to embed secret messages into digital media without changing their appearances. As a countermeasure to steganography, steganalysis detects the presence of hidden data in a digital content.
Zhao, Huimin, Jinchang Ren, Huimin Zhao, Zhao, Sophia, Stephen Marshall, Guoliang Xie, Sophia Zhao, Marshall, Stephen, Xie, Guoliang   +8 more
core   +1 more source

Rich Models for Steganalysis of Digital Images [PDF]

IEEE Transactions on Information Forensics and Security, 2012
We describe a novel general strategy for building steganography detectors for digital images. The process starts with assembling a rich model of the noise component as a union of many diverse submodels formed by joint distributions of neighboring samples from quantized image noise residuals obtained using linear and nonlinear high-pass filters.
Jessica J. Fridrich, Jan Kodovský
openaire   +1 more source

DrLS: Distortion‐Resistant Lossless Steganography via Colour Depth Interpolation

CAAI Transactions on Intelligence Technology, EarlyView.
ABSTRACT The lossless data steganography is to hide a certain amount of information into a container image. Previous lossless steganography methods fail to strike a balance between capacity, imperceptibility, accuracy, and robustness, commonly vulnerable to distortion on container images.
Youmin Xu, Xuanyu Zhang, Xiandong Meng, Jian Zhang   +3 more
wiley   +1 more source

Binary image steganographic techniques classification based on multi-class steganalysis

, 2010
In this paper, we propose a new multi-class steganalysis for binary image. The proposed method can identify the type of steganographic technique used by examining on the given binary image.
Kang Leng Chiew, Josef Pieprzyk, Chiew, Kang Leng, Pieprzyk, Josef   +3 more
core   +1 more source

Synthesis of Sustainable Chromium‐Based Nanoparticles With Fluorescence Tunable Ability for Biosensing of Tumor‐Derived Exosomes and Molecular Information Protection

SusMat, Volume 6, Issue 1, February 2026.
The study uses rapidly prepared Cr NPs with controllable fluorescence, enabling co‐regulation of quenching and enhancement for dyes and DNA of differing structures. By assembling a CD63‐specific aptamer with Cr NPs, it creates a quantitative readout for CD63‐positive TDEs.
Meng Yao Wu, Yu Qing Tian, Jiao Yang Lu, Tian Tian Fu, Hou Cheng Liang, Wei Tao Huang   +5 more
wiley   +1 more source

Steganography with High Reconstruction Robustness: Hiding of Encrypted Secret Images

Mathematics, 2022
As one of the important methods to protect information security, steganography can ensure the security of data in the process of information transmission, which has attracted much attention in the information security community.
Xishun Zhu, Zhengliang Lai, Nanrun Zhou, Jianhua Wu   +3 more
doaj   +1 more source

Revisiting weighted stego-image steganalysis

SPIE Proceedings, 2008
This paper revisits the steganalysis method involving a Weighted Stego-Image (WS) for estimating LSB replacement payload sizes in digital images. It suggests new WS estimators, upgrading the method's three components: cover pixel prediction, least-squares weighting, and bias correction.
Ker, A, Böhme, R
openaire   +1 more source

AGFP: A Deep Attention‐Guided Framework for DWT‐Based Image Steganography

IET Image Processing, Volume 20, Issue 1, January/December 2026.
This paper introduces Attention‐Guided Feature Perturbation (AGFP), a novel framework that combines deep learning‐based attention mechanisms with Discrete Wavelet Transform (DWT) embedding for image steganography. By selectively embedding data in perceptually and statistically safe regions, AGFP achieves high imperceptibility, robustness against ...
Taner Cevik, Nazife Cevik, Ali Pasaoglu, Fatih Sahin, Farzad Kiani, Muhammet Sait Ag   +5 more
wiley   +1 more source