ReST-Net: Diverse Activation Modules and Parallel Subnets-Based CNN for Spatial Image Steganalysis

Li, Bin; Wei, Weihang; Ferreira, Anselmo; Tan, Shunquan

doi:10.1109/lsp.2018.2816569

Cited by 96 publications

(43 citation statements)

References 19 publications

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“…The network also specify the significance of using larger amount of data samples for deeper networks and the benefits of alternative adaptive optimizer specially AdaDelta gradient decent variant. In 2018 ReSt-Net et al [43] explore another success approach by mean of diverse activation modules & parallel subnet-based CNN for spatial steganalysis. Their architecture consist of (DAMs) diverse activation modules, which activate the convolution outputs differently and then combine their outputs for the following layers.…”

Section: Spatial Steganalysis Methodsmentioning

confidence: 99%

A Survey on Deep Convolutional Neural Networks for Image Steganography and Steganalysis

2020

KSII TIIS

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Steganalysis & steganography have witnessed immense progress over the past few years by the advancement of deep convolutional neural networks (DCNN). In this paper, we analyzed current research states from the latest image steganography and steganalysis frameworks based on deep learning. Our objective is to provide for future researchers the work being done on deep learning-based image steganography & steganalysis and highlights the strengths and weakness of existing up-to-date techniques. The result of this study opens new approaches for upcoming research and may serve as source of hypothesis for further significant research on deep learning-based image steganography and steganalysis. Finally, technical challenges of current methods and several promising directions on deep learning steganography and steganalysis are suggested to illustrate how these challenges can be transferred into prolific future research avenues.

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Section: Spatial Steganalysis Methodsmentioning

confidence: 99%

A Survey on Deep Convolutional Neural Networks for Image Steganography and Steganalysis

2020

KSII TIIS

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“…The SRM is highly accurate compared to CNN-based methods. The method of extracting many features using various types of HPFs has also been widely used in CNN-based ones [19,20,[25][26][27]].…”

Section: Srmmentioning

confidence: 99%

“…Yuan et al used the same network structure as the initial CNN, but utilized three HPFs in a preprocessing stage [18]. ReST-Net [19] uses three different filter sets, namely 16 simplified linear SRM, 14 nonlinear SRM, and 16 Gabor filters (Figures 3 and 5) in the preprocessing stage to extract much more features from the input images. In addition, ReST-Net constructs three subnetworks ( Figure 6).…”

Section: Cnn-based Image Steganalysismentioning

confidence: 99%

“…After separately training the subnetworks using each preprocessing filter, it trains a new fully connected layer using transfer learning while fixing the parameters of three subnetworks. [19], which comprises three three subnetworks that are a modification of the initial CNN [17] and uses transfer learning.…”

Section: Cnn-based Image Steganalysismentioning

confidence: 99%

“…With the great success of convolutional neural networks (CNN) in object detection and recognition [15,16], using CNNs for steganalysis has been actively investigated [17][18][19][20][21][22][23][24][25][26][27]. Unlike handcrafted feature-based methods, a CNN can automatically extract and learn the features that are optimal or well suited for identifying steganographic methods.…”

Section: Introductionmentioning

confidence: 99%

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CNN-Based Ternary Classification for Image Steganalysis

Kang

Park

2019

Electronics

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This study proposes a convolutional neural network (CNN)-based steganalytic method that allows ternary classification to simultaneously identify WOW and UNIWARD, which are representative adaptive image steganographic algorithms. WOW and UNIWARD have very similar message embedding methods in terms of measuring and minimizing the degree of distortion of images caused by message embedding. This similarity between WOW and UNIWARD makes it difficult to distinguish between both algorithms even in a CNN-based classifier. Our experiments particularly show that WOW and UNIWARD cannot be distinguished by simply combining binary CNN-based classifiers learned to separately identify both algorithms. Therefore, to identify and classify WOW and UNIWARD, WOW and UNIWARD must be learned at the same time using a single CNN-based classifier designed for ternary classification. This study proposes a method for ternary classification that learns and classifies cover, WOW stego, and UNIWARD stego images using a single CNN-based classifier. A CNN structure and a preprocessing filter are also proposed to effectively classify/identify WOW and UNIWARD. Experiments using BOSSBase 1.01 database images confirmed that the proposed method could make a ternary classification with an accuracy of approximately 72%.

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Evaluation of Deep Learning and Conventional Approaches for Image Steganalysis

Xie

Ren

Zhao

et al. 2020

Advances in Brain Inspired Cognitive Systems

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Steganography is the technique for embedding secret messages into digital media without changing their appearances. As a countermeasure to steganography, steganalysis detects the presence of hidden data in digital content. For the last decade, the majority of image steganalysis approaches can be formed by two stages. The first stage is to extract effective features from the image content and the second is to train a classifier in machine learning by using the features from stage one. Ultimately the image steganalysis becomes a binary classification problem. Since Deep Learning related architecture unifies these two stages and saves researchers lots of time designing hand-crafted features, the design of a CNN-based steganalyzer has therefore received increasing attention over the past few years. In this paper, we will examine the development in image steganalysis, both in the spatial domain and in the JPEG domain, and discuss the future directions.

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ReST-Net: Diverse Activation Modules and Parallel Subnets-Based CNN for Spatial Image Steganalysis

Cited by 96 publications

References 19 publications

A Survey on Deep Convolutional Neural Networks for Image Steganography and Steganalysis

A Survey on Deep Convolutional Neural Networks for Image Steganography and Steganalysis

CNN-Based Ternary Classification for Image Steganalysis

Evaluation of Deep Learning and Conventional Approaches for Image Steganalysis

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