Improving Steganographic Security by Synchronizing the Selection Channel

Denemark, Tomáš; Fridrich, Jessica

doi:10.1145/2756601.2756620

Cited by 101 publications

(68 citation statements)

References 18 publications

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“…The second method is the Wavelet Obtained Weights WOW steganography algorithm . Finally, the third method is the Synchronizing the Selection Channel Synch‐HILL steganography algorithm .…”

Section: Resultsmentioning

confidence: 99%

Color images steganalysis using rgb channel geometric transformation measures

Abdulrahman

Chaumont

Montesinos

et al. 2016

Security Comm Networks

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In recent years, information security has received a great deal of attention. To give an example, steganography techniques are used to communicate in a secret and invisible way. Digital color images have become a good medium for digital steganography because of their easy manipulation as carriers via Internet, e‐mails, or used on websites. The main goal of steganalysis is to detect the presence of hidden messages in a digital media. The proposed method is a further extension of the authors' previous work: steganalysis based on color feature correlation and machine learning classification. Fusing features with those obtained from color‐rich models allows increasing the detectability of hidden messages in the color images. Our new proposition uses two types of features, computed between color image channels. The first type of feature reflects local Euclidean transformations, and the second one reflects mirror transformations. These geometric measures are obtained by the sine and cosine of gradient angles between all the color channels. Features are extracted from co‐occurrence correlation matrices of measures. We demonstrate the efficiency of the proposed framework on three steganography algorithms designed to hide messages in images represented in the spatial domain: S‐UNIWARD, WOW, and Synch‐HILL. For each algorithm, we applied a range of different payload sizes. The efficiency of the proposed method is demonstrated by the comparison with the previous authors work and the spatial color‐rich model and color filter array‐aware features for steganalysis. Copyright © 2016 John Wiley & Sons, Ltd.

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“…The second method is the Wavelet Obtained Weights WOW steganography algorithm . Finally, the third method is the Synchronizing the Selection Channel Synch‐HILL steganography algorithm .…”

Section: Resultsmentioning

confidence: 99%

Color images steganalysis using rgb channel geometric transformation measures

Abdulrahman

Chaumont

Montesinos

et al. 2016

Security Comm Networks

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“…Furthermore, the final prediction conducted by the ensemble of five independently trained models shows that model ensemble could improve the detection accuracy by 1% regardless of the type of the underlying subnet configurations. 8 Since the performance of our proposed framework with Type1 subnets is always better than that with Type2 subnets, we insisted on using Type1 subnets in the following experiments. However, please note that Type2 subnet can potentially be used in more complex deep-learning steganalytic frameworks in the future since it is a memory-efficient model.…”

Section: Comparison To State Of the Artmentioning

confidence: 99%

“…Please note that we also set "stride=2" in the bottom convolutional layer of Type1 and Type2 subnets of our proposed framework. 8 Please note that the ensemble approach of Xu's model [24] can also probably obtain better results. The experimental results of ensemble prediction of Xu's model are omitted in Fig.…”

Section: Comparison To State Of the Artmentioning

confidence: 99%

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Large-Scale JPEG Image Steganalysis Using Hybrid Deep-Learning Framework

Zeng

Tan

et al. 2018

IEEE Trans.Inform.Forensic Secur.

184

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Abstract-Adoption of deep learning in image steganalysis is still in its initial stage. In this paper we propose a generic hybrid deep-learning framework for JPEG steganalysis incorporating the domain knowledge behind rich steganalytic models. Our proposed framework involves two main stages. The first stage is hand-crafted, corresponding to the convolution phase and the quantization & truncation phase of the rich models. The second stage is a compound deep neural network containing multiple deep subnets in which the model parameters are learned in the training procedure. We provided experimental evidences and theoretical reflections to argue that the introduction of threshold quantizers, though disable the gradient-descent-based learning of the bottom convolution phase, is indeed cost-effective. We have conducted extensive experiments on a large-scale dataset extracted from ImageNet. The primary dataset used in our experiments contains 500,000 cover images, while our largest dataset contains five million cover images. Our experiments show that the integration of quantization and truncation into deeplearning steganalyzers do boost the detection performance by a clear margin. Furthermore, we demonstrate that our framework is insensitive to JPEG blocking artifact alterations, and the learned model can be easily transferred to a different attacking target and even a different dataset. These properties are of critical importance in practical applications.

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“…All of the state-of-the-art steganographic algorithms which can be applied on color images ( [1], [2], [5]- [7]) add zero-mean ±1 additional stego noise to every target band of a given cover image. Therefore, every band in X can be further represented…”

Section: A Preliminariesmentioning

confidence: 99%

WISERNet: Wider Separate-Then-Reunion Network for Steganalysis of Color Images

Zeng

Tan

Liu

et al. 2019

IEEE Trans.Inform.Forensic Secur.

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Until recently, deep steganalyzers in spatial domain have been all designed for gray-scale images. In this paper, we propose WISERNet (the wider separate-then-reunion network) for steganalysis of color images. We provide theoretical rationale to claim that the summation in normal convolution is one sort of "linear collusion attack" which reserves strong correlated patterns while impairs uncorrelated noises. Therefore in the bottom convolutional layer which aims at suppressing correlated image contents, we adopt separate channel-wise convolution without summation instead. Conversely, in the upper convolutional layers we believe that the summation in normal convolution is beneficial. Therefore we adopt united normal convolution in those layers and make them remarkably wider to reinforce the effect of "linear collusion attack". As a result, our proposed wide-and-shallow, separate-then-reunion network structure is specifically suitable for color image steganalysis. We have conducted extensive experiments on color image datasets generated from BOSSBase raw images and another large-scale dataset which contains 100,000 raw images, with different demosaicking algorithms and downsampling algorithms. The experimental results show that our proposed network outperforms other state-of-the-art color image steganalytic models either hand-crafted or learned using deep networks in the literature by a clear margin. Specifically, it is noted that the detection performance gain is achieved with less than half the complexity compared to the most advanced deeplearning steganalyzer as far as we know, which is scarce in the literature.

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Improving Steganographic Security by Synchronizing the Selection Channel

Cited by 101 publications

References 18 publications

Color images steganalysis using rgb channel geometric transformation measures

Color images steganalysis using rgb channel geometric transformation measures

Large-Scale JPEG Image Steganalysis Using Hybrid Deep-Learning Framework

WISERNet: Wider Separate-Then-Reunion Network for Steganalysis of Color Images

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