Pooled Steganalysis in JPEG: how to deal with the spreading strategy?

Zakaria, Ahmad; Chaumont, Marc; Subsol, Gérard

doi:10.1109/wifs47025.2019.9035096

Cited by 5 publications

(1 citation statement)

References 22 publications

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“…Nor will we discuss batch steganography and pooled steganalysis with CNNs which has not yet been addressed, although the work presented in [112] using two-stage machine learning can be extended to deep learning.…”

Section: The Different Network Used Over the Period 2015-2018mentioning

confidence: 99%

Deep Learning in steganography and steganalysis from 2015 to 2018

Chaumont¹

2019

Preprint

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For almost 10 years, the detection of a hidden message in an image has been mainly carried out by the computation of Rich Models (RM), followed by classification using an Ensemble Classifier (EC). In 2015, the first study using a convolutional neural network (CNN) obtained the first results of steganalysis by Deep Learning approaching the performances of the two-step approach (EC + RM). Between 2015-2018, numerous publications have shown that it is possible to obtain improved performances, notably in spatial steganalysis, JPEG steganalysis, Selection-Channel-Aware steganalysis, and in quantitative steganalysis. This chapter deals with deep learning in steganalysis from the point of view of current methods, by presenting different neural networks from the period 2015-2018, that have been evaluated with a methodology specific to the discipline of steganalysis. The chapter is not intended to repeat the basic concepts of machine learning or deep learning. So, we will present the structure of a deep neural network, in a generic way and present the networks proposed in existing literature for the different scenarios of steganalysis, and finally, we will discuss steganography by deep learning.

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Section: The Different Network Used Over the Period 2015-2018mentioning

confidence: 99%

Deep Learning in steganography and steganalysis from 2015 to 2018

Chaumont¹

2019

Preprint

Self Cite

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Quantized Gaussian JPEG Steganography and Pool Steganalysis

2022

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Currently, algorithms for compressed image steganography mainly embed hidden message by minimizing the resulting distortion or statistical detectability. However, as a result of purely heuristic distortion definitions and numerically solvable equations in statistical models, there are no closed-form solutions for JPEG steganography. The absence of closed-form expression to model JPEG steganography is the main limitation on understanding single image and pool steganalysis behavior. In this study, building upon our previously proposed framework for spatial steganography, we develop a statistical framework for JPEG steganography in which the cover and the hidden message are modeled by multivariate Gaussian distribution. Based on this statistical model, we propose a novel quantized Gaussian JPEG steganography that is able to accomplish embedding using any costs defined in spatial or discrete cosine transform (DCT) domain as well as residual variances. We conduct our experiments using a popular database with different compression qualities to determine the effectiveness of the proposed model. The experimental results show that the proposed model improves the security of previous works and outperforms the state-of-theart JPEG steganography algorithms. Furthermore, we extend the closed-form expression of single image steganalysis error to pool steganalysis for an omniscience optimal detector. We employ the derived expression to approximate the empirical results of pool steganalysis based on the empirical detection error of single image steganalysis. The practical advantage of the approximation is that even though it is derived based on the adopted statistical model, it is accurate regardless of payload, embedding domain, embedding method, and steganalysis feature as long as the pooling strategy is optimal. In addition to approximation of the error, we employ the proposed model to make predictions about the variance behavior of pool steganalysis error. We mathematically show that the variance increases as the pool size increases in small payloads. The same behavior is observed in experimental results which re-validates our analytical model. We conclude that although pooling improves detector's performance, it makes the detector less stable in low payloads and high pool sizes.INDEX TERMS JPEG steganography, optimal detector, quantized gaussian embedding, pool steganalysis.

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Efficient JPEG Batch Steganography Using Intrinsic Energy of Image Contents

Ni²,

Zhang

et al. 2021

IEEE Trans.Inform.Forensic Secur.

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Pooled Steganalysis in JPEG: how to deal with the spreading strategy?

Cited by 5 publications

References 22 publications

Deep Learning in steganography and steganalysis from 2015 to 2018

Deep Learning in steganography and steganalysis from 2015 to 2018

Quantized Gaussian JPEG Steganography and Pool Steganalysis

Efficient JPEG Batch Steganography Using Intrinsic Energy of Image Contents

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