Journal ArticleDOI
Deep Learning Hierarchical Representations for Image Steganalysis
Jian Ye,Jiangqun Ni,Yang Yi +2 more
TLDR
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.Abstract:
Nowadays, the prevailing detectors of steganographic communication in digital images mainly consist of three steps, ie, residual computation, feature extraction, and binary classification In this paper, we present 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 The proposed CNN has a quite different structure from the ones used in conventional computer vision tasks Rather than a random strategy, the weights in the first layer of the proposed CNN are initialized with the basic high-pass filter set used in the calculation of residual maps in a spatial rich model (SRM), which acts as a regularizer to suppress the image content effectively To better capture the structure of embedding signals, which usually have extremely low SNR (stego signal to image content), a new activation function called a truncated linear unit is adopted in our CNN model Finally, we further boost the performance of the proposed CNN-based steganalyzer by incorporating the knowledge of selection channel Three state-of-the-art steganographic algorithms in spatial domain, eg, WOW, S-UNIWARD, and HILL, are used to evaluate the effectiveness of our model Compared to SRM and its selection-channel-aware variant maxSRMd2, our model achieves superior performance across all tested algorithms for a wide variety of payloadsread more
Citations
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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
A Novel Image Steganography Method via Deep Convolutional Generative Adversarial Networks
TL;DR: A novel image SWE method based on deep convolutional generative adversarial networks that has the advantages of highly accurate information extraction and a strong ability to resist detection by state-of-the-art image steganalysis algorithms.
Journal ArticleDOI
Depth-Wise Separable Convolutions and Multi-Level Pooling for an Efficient Spatial CNN-Based Steganalysis
TL;DR: The experimental results show that the proposed CNN structure is significantly better than other five methods when it is used to detect three spatial algorithms such as WOW, S-UNIWARD and HILL with a wide variety of datasets and payloads.
Journal ArticleDOI
A fusion steganographic algorithm based on Faster R-CNN
TL;DR: Experimental results show that this approach enhances the security and provides robust embedding of secret data in image, video, voice or text media.
Journal ArticleDOI
An Embedding Cost Learning Framework Using GAN
TL;DR: A distortion function generating a framework for steganography that outperforms the current state-of-the-art steganographic schemes and the adversarial training time is reduced dramatically compared with the GAN-based automatic Steganographic distortion learning framework (ASDL-GAN).
References
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ImageNet Classification with Deep Convolutional Neural Networks
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Posted Content
Caffe: Convolutional Architecture for Fast Feature Embedding
Yangqing Jia,Evan Shelhamer,Jeff Donahue,Sergey Karayev,Jonathan Long,Ross Girshick,Sergio Guadarrama,Trevor Darrell +7 more
TL;DR: Caffe as discussed by the authors is a BSD-licensed C++ library with Python and MATLAB bindings for training and deploying general-purpose convolutional neural networks and other deep models efficiently on commodity architectures.
Posted Content
Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification
TL;DR: This work proposes a Parametric Rectified Linear Unit (PReLU) that generalizes the traditional rectified unit and derives a robust initialization method that particularly considers the rectifier nonlinearities.