Robust Steganography Using Texture Synthesis

Qian, Zhenxing; Zhou, Hang; Zhang, Weimin; Zhang, Xinpeng

doi:10.1007/978-3-319-50209-0_4

Cited by 41 publications

(10 citation statements)

References 8 publications

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“…Wu et al [49] proposed a reversible texture image synthesis for steganography. Qian et al [50] propose a steganography method which secret messages are hidden in a texture image during the process of synthesizing. [51,52] which does this by generating samples rather than estimating the specific form of the distribution.…”

Section: Fig 4 Steganography With Cover Synthesismentioning

confidence: 99%

Recent Advances of Image Steganography With Generative Adversarial Networks

et al. 2020

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In the past few years, the Generative Adversarial Network (GAN) which proposed in 2014 has achieved great success. GAN has achieved many research results in the field of computer vision and natural language processing. Image steganography is dedicated to hiding secret messages in digital images, and has achieved the purpose of covert communication. Recently, research on image steganography has demonstrated great potential for using GAN and neural networks. In this paper we review different strategies for steganography such as cover modification, cover selection and cover synthesis by GANs, and discuss the characteristics of these methods as well as evaluation metrics and provide some possible future research directions in image steganography.

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Section: Fig 4 Steganography With Cover Synthesismentioning

confidence: 99%

Recent Advances of Image Steganography With Generative Adversarial Networks

et al. 2020

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“…With the help of texture synthesis, the authors of [6,7] used a texture sample and a set of color points generated based on a secret messages to construct a dense texture images. Qian, et al [8,9] proposed a robust method of steganography based on texture synthesis. Xue, et al [10] used marbling, a unique texture synthesis method that allows users to deliver personalized messages hidden within beautiful, decorative textures.…”

Section: Related Workmentioning

confidence: 99%

Generative Steganography by Sampling

Zhang

Yan

et al. 2019

IEEE Access

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In this paper, a novel data-driven information hiding scheme called "generative steganography by sampling" (GSS) is proposed. Unlike in traditional modification-based steganography, in our method the stego image is directly sampled by a powerful generator: no explicit cover is used. Both parties share a secret key used for message embedding and extraction. The Jensen-Shannon divergence is introduced as a new criterion for evaluating the security of generative steganography. Based on these principles, we propose a simple practical generative steganography method that uses semantic image inpainting. The message is written in advance to an uncorrupted region that needs to be retained in the corrupted image. Then, the corrupted image with the secret message is fed into a Generator trained by a generative adversarial network (GAN) for semantic completion. Message loss and prior loss terms are proposed for penalizing message extraction error and unrealistic stego image. In our design, we first train a generator whose training target is the generation of new data samples from the same distribution as that of existing training data. Next, for the trained generator, backpropagation to the message and prior loss are introduced to optimize the coding of the input noise data for the generator. The presented experiments demonstrate the potential of the proposed framework based on both qualitative and quantitative evaluations of the generated stego images. INDEX TERMS Generative adversarial network, Image inpainting, Steganography

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“…It is more flexible but also challenging. In preliminary studies, researchers tried unnatural image synthesis for information hiding, such as texture [23]- [29] and S. Zhang, Z. Yang, J. Yang and Y. Huang were with the Department of Electronic Engineering, Tsinghua University, Beijing, China. E-mail: yangzl15@tsinghua.org.cn.…”

Section: Introductionmentioning

confidence: 99%

Pixel-Stega: Generative Image Steganography Based on Autoregressive Models

Zhang¹,

Yang²,

Tu³

et al. 2021

Preprint

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In this letter, we explored generative image steganography based on autoregressive models. We proposed Pixel-Stega, which implements pixel-level information hiding with autoregressive models and arithmetic coding algorithm. Firstly, one of the autoregressive models, PixelCNN++, is utilized to produce explicit conditional probability distribution of each pixel. Secondly, secret messages are encoded to the selection of pixels through steganographic sampling (stegosampling) based on arithmetic coding. We carried out qualitative and quantitative assessment on gray-scale and colour image datasets. Experimental results show that Pixel-Stega is able to embed secret messages adaptively according to the entropy of the pixels to achieve both high embedding capacity (up to 4.3 bpp) and nearly perfect imperceptibility (about 50% detection accuracy). 1

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Robust Steganography Using Texture Synthesis

Cited by 41 publications

References 8 publications

Recent Advances of Image Steganography With Generative Adversarial Networks

Recent Advances of Image Steganography With Generative Adversarial Networks

Generative Steganography by Sampling

Pixel-Stega: Generative Image Steganography Based on Autoregressive Models

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