Journal ArticleDOI
Adaptive Batch Size Image Merging Steganography and Quantized Gaussian Image Steganography
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TLDR
Adaptive Batch size Image Merging steganographer, AdaBIM is introduced and mathematically prove it outperforms the state-of-the-art batch steganography method and further verify its superiority by experiments.Abstract:
In digital image steganography, the statistical model of an image is essential for hiding data in less detectable regions and achieving better security. This has been addressed in the literature where different cost-based and statistical model-based approaches were proposed. However, due to the usage of heuristically defined distortions and non-constrained message models, resulting in numerically solvable equations, there is no closed-form expression for security as a function of payload. The closed-form expression is crucial for a better insight into image steganography problem and also improving performance of batch steganography algorithms. Here, we develop a statistical framework for image steganography in which the cover and the stego messages are modeled as multivariate Gaussian random variables. We propose a novel Gaussian embedding model by maximizing the detection error of the most common optimal detectors within the adopted statistical model. Furthermore, we extend the formulation to cost-based steganography, resulting in a universal embedding scheme that improves empirical results of current cost-based and statistical model-based approaches. This methodology and its presented solution, by reason of assuming a continuous hidden message, remains the same for any embedding scenario. Afterward, the closed-form detection error is derived within the adopted model for image steganography and it is extended to batch steganography. Thus, we introduce Adaptive Batch size Image Merging steganographer, AdaBIM , and mathematically prove it outperforms the state-of-the-art batch steganography method and further verify its superiority by experiments.read more
Citations
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Book ChapterDOI
Coding Theorems for a Discrete Source With a Fidelity CriterionInstitute of Radio Engineers, International Convention Record, vol. 7, 1959.
Neil J. A. Sloane,Aaron D. Wyner +1 more
TL;DR: For a wide class of distortion measures and discrete sources of information there exists a functionR(d) (depending on the particular distortion measure and source) which measures the equivalent rateR of the source (in bits per letter produced) whendis the allowed distortion level.
Journal ArticleDOI
A Novel Image Steganography Method for Industrial Internet of Things Security
TL;DR: Using the HHO-IWT method for covert communication and secure data in the IIoT environment based on digital image steganography achieves higher levels of security than the state-of-the-art methods and it resists various forms of steganalysis.
Journal ArticleDOI
Analysis of various data security techniques of steganography: A survey
Sachin Dhawan,Rashmi Gupta +1 more
TL;DR: The objective of the paper is to examine and scrutinize the different algorithms of steganography based on parameters like PSNR, MSE, and Robustness and make recommendations for producing high-quality stego images, high-payload capacity, and robust techniques of Steganography.
Journal ArticleDOI
Neural Style Transfer for image within images and conditional GANs for destylization
TL;DR: In this paper , the secret content of the secret image in the in-between hidden layered style features of the cover image, which is the first step in the present state-of-the-art steganographic framework.
Journal ArticleDOI
Determination of oil well placement using convolutional neural network coupled with robust optimization under geological uncertainty
TL;DR: The findings highlight that the integrated approach can be used as an efficient decision-making tool for solving well-placement optimization problems with accuracy at an affordable computational cost.
References
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Journal ArticleDOI
Review: Digital image steganography: Survey and analysis of current methods
TL;DR: This paper provides a state-of-the-art review and analysis of the different existing methods of steganography along with some common standards and guidelines drawn from the literature and some recommendations and advocates for the object-oriented embedding mechanism.
Book ChapterDOI
The Prisoners’ Problem and the Subliminal Channel
TL;DR: Two accomplices in a crime have been arrested and are about to be locked in widely separated cells and their only means of communication after they are locked up will he by way of messages conveyed for them by trustees -- who are known to be agents of the warden.
Journal ArticleDOI
Ensemble Classifiers for Steganalysis of Digital Media
TL;DR: This paper proposes an alternative and well-known machine learning tool-ensemble classifiers implemented as random forests-and argues that they are ideally suited for steganalysis.
Journal ArticleDOI
Steganalysis by Subtractive Pixel Adjacency Matrix
TL;DR: A method for detection of steganographic methods that embed in the spatial domain by adding a low-amplitude independent stego signal, an example of which is least significant bit (LSB) matching.