Abstract:For about 10 years, detecting the presence of a secret message hidden in an image was performed with an Ensemble Classifier trained with Rich features. In recent years, studies such as Xu et al. have indicated that well-designed convolutional Neural Networks (CNN) can achieve comparable performance to the two-step machine learning approaches.In this paper, we propose a CNN that outperforms the state-ofthe-art in terms of error probability. The proposition is in the continuity of what has been recently proposed… Show more
“…It performs slightly better than PSRM [27]. Yedroudj-Net et al [23], proposed CNN framework that outperforms in term of the error probability. Experiments were performed to show its supremacy with other state-of the art framework like Xu-net [35], & Ye-Net [28] in its not informed version and to Ensemble Classifier fed by the Spatial Rich Model [33].…”
Section: Spatial Steganalysis Methodsmentioning
confidence: 99%
“…Experiments were performed to show its supremacy with other state-of the art framework like Xu-net [35], & Ye-Net [28] in its not informed version and to Ensemble Classifier fed by the Spatial Rich Model [33]. In 2018, Zhang et al [10], proposed an adequate feature learning & multi-size image steganalysis framework based on CNN called Zhu-net and the proposed network achieved better detection performance compared to Yedrouudj-Net [23]. Zhu-Net offer three improvement in Yedroudj-Net that are the renovate the kernel filters of pre-processinglayer, secondly replace the first two convolutional blocks with two module of depth-wise detachable convolutions that can extract the spatial and channel correlation of residuals to increase SNR and obviously improve the accuracy, finally replace the gobal pooling with spatial pyramid pooling to deal with arbitrary images.…”
Section: Spatial Steganalysis Methodsmentioning
confidence: 99%
“…Best CNN framework for JPEG as well as spatial steganalysis at the end of 2018 is SR-Net that has been proposed with side-channel-information [8]. The network is corresponding to the combination of convolutional blocks beyond the pooling layer immediately after the first convolution block of the Yedrouj-Net [23]. Essential part of SR-Net is noise residual extraction section consist of first seven layers.…”
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.
“…It performs slightly better than PSRM [27]. Yedroudj-Net et al [23], proposed CNN framework that outperforms in term of the error probability. Experiments were performed to show its supremacy with other state-of the art framework like Xu-net [35], & Ye-Net [28] in its not informed version and to Ensemble Classifier fed by the Spatial Rich Model [33].…”
Section: Spatial Steganalysis Methodsmentioning
confidence: 99%
“…Experiments were performed to show its supremacy with other state-of the art framework like Xu-net [35], & Ye-Net [28] in its not informed version and to Ensemble Classifier fed by the Spatial Rich Model [33]. In 2018, Zhang et al [10], proposed an adequate feature learning & multi-size image steganalysis framework based on CNN called Zhu-net and the proposed network achieved better detection performance compared to Yedrouudj-Net [23]. Zhu-Net offer three improvement in Yedroudj-Net that are the renovate the kernel filters of pre-processinglayer, secondly replace the first two convolutional blocks with two module of depth-wise detachable convolutions that can extract the spatial and channel correlation of residuals to increase SNR and obviously improve the accuracy, finally replace the gobal pooling with spatial pyramid pooling to deal with arbitrary images.…”
Section: Spatial Steganalysis Methodsmentioning
confidence: 99%
“…Best CNN framework for JPEG as well as spatial steganalysis at the end of 2018 is SR-Net that has been proposed with side-channel-information [8]. The network is corresponding to the combination of convolutional blocks beyond the pooling layer immediately after the first convolution block of the Yedrouj-Net [23]. Essential part of SR-Net is noise residual extraction section consist of first seven layers.…”
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.
“…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%
“…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.…”
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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