Proceedings ArticleDOI
Deep learning for steganalysis via convolutional neural networks
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TLDR
A new paradigm for steganalysis to learn features automatically via deep learning models through a customized Convolutional Neural Network that achieves comparable performance on BOSSbase and the realistic and large ImageNet database.Abstract:
Current work on steganalysis for digital images is focused on the construction of complex handcrafted features. This paper proposes a new paradigm for steganalysis to learn features automatically via deep learning models. We novelly propose a customized Convolutional Neural Network for steganalysis. The proposed model can capture the complex dependencies that are useful for steganalysis. Compared with existing schemes, this model can automatically learn feature representations with several convolutional layers. The feature extraction and classification steps are unified under a single architecture, which means the guidance of classification can be used during the feature extraction step. We demonstrate the effectiveness of the proposed model on three state-of-theart spatial domain steganographic algorithms - HUGO, WOW, and S-UNIWARD. Compared to the Spatial Rich Model (SRM), our model achieves comparable performance on BOSSbase and the realistic and large ImageNet database. © (2015) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.read more
Citations
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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.
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
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
Noiseprint: A CNN-Based Camera Model Fingerprint
Davide Cozzolino,Luisa Verdoliva +1 more
TL;DR: In this article, a Siamese network is used to extract a camera model fingerprint, where the scene content is largely suppressed and model-related artifacts are enhanced, which can be used for a large variety of forensic tasks, including image forgery localization.
References
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TL;DR: In this article, a graph transformer network (GTN) is proposed for handwritten character recognition, which can be used to synthesize a complex decision surface that can classify high-dimensional patterns, such as handwritten characters.
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