Deep Residual Network for Steganalysis of Digital Images
TLDR
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.Abstract:
Steganography detectors built as deep convolutional neural networks have firmly established themselves as superior to the previous detection paradigm – classifiers based on rich media models. Existing network architectures, however, still contain elements designed by hand, such as fixed or constrained convolutional kernels, heuristic initialization of kernels, the thresholded linear unit that mimics truncation in rich models, quantization of feature maps, and awareness of JPEG phase. In this work, we describe 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. The key part of the proposed architecture is a significantly expanded front part of the detector that “computes noise residuals” in which pooling has been disabled to prevent suppression of the stego signal. Extensive experiments show the superior performance of this network with a significant improvement, especially in the JPEG domain. Further performance boost is observed by supplying the selection channel as a second channel.read more
Citations
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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
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).
Journal ArticleDOI
A Siamese CNN for Image Steganalysis
TL;DR: This paper proposes an end-to-end, deep learning, novel solution for distinguishing steganography images from normal images that provides satisfying performance and adopts a Siamese, CNN-based architecture.
Journal ArticleDOI
Enhanced embedding capacity for the SMSD-based data-hiding method
TL;DR: A new definition of the enhanced MSD (EMSD) representation is introduced and an EMSD-based data-hiding scheme is proposed that improves the embedding capacity of SMSD data hiding and maintains equal quality of the stego-image.
Journal ArticleDOI
An Automatic Cost Learning Framework for Image Steganography Using Deep Reinforcement Learning
TL;DR: A new embedding cost learning framework called SPAR-RL (Steganographic Pixel-wise Actions and Rewards with Reinforcement Learning) that achieves state-of-the-art security performance against various modern steganalyzers, and outperforms existing cost learning frameworks with regard to learning stability and efficiency.
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