Journal ArticleDOI
Generative focused feedback residual networks for image steganalysis and hidden information reconstruction
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This article is published in Social Science Research Network.The article was published on 2022-08-01. It has received 1 citations till now. The article focuses on the topics: Computer science & Steganalysis.read more
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Journal ArticleDOI
Self-attention enhanced deep residual network for spatial image steganalysis
TL;DR: Li et al. as discussed by the authors proposed an enhanced residual network (ERANet) with selfattention ability, which utilizes a more complex residual method and a global self-attention technique, to alleviate the problem.
References
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Journal ArticleDOI
Rich Models for Steganalysis of Digital Images
Jessica Fridrich,Jan Kodovsky +1 more
TL;DR: A novel general strategy for building steganography detectors for digital images by assembling a rich model of the noise component as a union of many diverse submodels formed by joint distributions of neighboring samples from quantized image noise residuals obtained using linear and nonlinear high-pass filters.
Journal ArticleDOI
Steganalysis by Subtractive Pixel Adjacency Matrix
TL;DR: A method for detection of steganographic methods that embed in the spatial domain by adding a low-amplitude independent stego signal, an example of which is least significant bit (LSB) matching.
Journal ArticleDOI
Universal distortion function for steganography in an arbitrary domain
TL;DR: This paper proposes a universal distortion design called universal wavelet relative distortion (UNIWARD) that can be applied for embedding in an arbitrary domain and demonstrates experimentally using rich models as well as targeted attacks that steganographic methods built using UNIWARD match or outperform the current state of the art in the spatial domain, JPEG domain, and side-informed JPEG domain.
Journal ArticleDOI
Structural Design of Convolutional Neural Networks for Steganalysis
TL;DR: 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.
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.
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