Journal ArticleDOI
Structural Design of Convolutional Neural Networks for Steganalysis
TLDR
Although it learns from only one type of noise residual, the proposed CNN is competitive in terms of detection performance compared with the SRM with ensemble classifiers on the BOSSbase for detecting S-UNIWARD and HILL.Abstract:
Recent studies have indicated that the architectures of convolutional neural networks (CNNs) tailored for computer vision may not be best suited to image steganalysis. In this letter, we report a CNN architecture that takes into account knowledge of steganalysis. In the detailed architecture, we take absolute values of elements in the feature maps generated from the first convolutional layer to facilitate and improve statistical modeling in the subsequent layers; to prevent overfitting, we constrain the range of data values with the saturation regions of hyperbolic tangent ( TanH ) at early stages of the networks and reduce the strength of modeling using $1\times1$ convolutions in deeper layers. Although it learns from only one type of noise residual, the proposed CNN is competitive in terms of detection performance compared with the SRM with ensemble classifiers on the BOSSbase for detecting S-UNIWARD and HILL. The results have implied that well-designed CNNs have the potential to provide a better detection performance in the future.read more
Citations
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Journal ArticleDOI
Deep Learning Hierarchical Representations for Image Steganalysis
Jian Ye,Jiangqun Ni,Yang Yi +2 more
TL;DR: This paper presents an alternative approach to steganalysis of digital images based on convolutional neural network (CNN), which is shown to be able to well replicate and optimize these key steps in a unified framework and learn hierarchical representations directly from raw images.
Journal ArticleDOI
Deep Residual Network for Steganalysis of Digital Images
TL;DR: A deep residual architecture designed to minimize the use of heuristics and externally enforced elements that is universal in the sense that it provides state-of-the-art detection accuracy for both spatial-domain and JPEG steganography.
Journal ArticleDOI
Constrained Convolutional Neural Networks: A New Approach Towards General Purpose Image Manipulation Detection
Belhassen Bayar,Matthew C. Stamm +1 more
TL;DR: This paper has developed a new type of CNN layer, called a constrained convolutional layer, that is able to jointly suppress an image’s content and adaptively learn manipulation detection features.
Journal ArticleDOI
Deep residual learning for image steganalysis
TL;DR: Comprehensive experiments show that the proposed Deep Residual learning based Network (DRN) model can detect the state of arts steganographic algorithms at a high accuracy and outperforms the classical rich model method and several recently proposed CNN based methods.
Journal ArticleDOI
Automatic Steganographic Distortion Learning Using a Generative Adversarial Network
TL;DR: Experimental results show that the proposed automatic steganographic distortion learning framework can effectively evolve from nearly naïve random $\pm 1$ embedding at the beginning to much more advanced content-adaptive embedding which tries to embed secret bits in textural regions.
References
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