Hidden message in a deformation-based texture

Xu, Jiayi; Mao, Xiaoyang; Jin, Xiaogang; Jaffer, Aubrey; Lu, Shufang; Li, Li; Toyoura, Masahiro

doi:10.1007/s00371-014-1045-z

Cited by 55 publications

(22 citation statements)

References 30 publications

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“…Qian et al [50] propose a steganography method which secret messages are hidden in a texture image during the process of synthesizing. [51,52] introduce a deformation-based texture for information hiding. Zhang et al [53] propose a construction-based steganography scheme which conceals a secret message into a fingerprint image.…”

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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“…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. This type of texture-based steganography is based on the premise that the cover does not have to represent real-world content (which is counterintuitive for steganography, whose objective is typically to maintain the nature of the cover).…”

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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“…Otori and Kuriyama's scheme was improved by Wu and Wang in [38], but the new scheme was shown insecure by Zhou et al [46]. In [41] a different scheme of generating marbling textures to deliver hidden information. More recently, machine learning based approaches have been considered to generate covers for steganography [7,34,36].…”

Section: Generative (Coverless) Information Hidingmentioning

confidence: 99%

“…Therefore, it is necessary to invent new steganographic methods to make information hiding more secure. One new approach called generative (also known as coverless) information hiding is to move away from using a selected (pre-existing) cover object, but to embed hidden data into one or more generated objects that did not exist [7,26,27,34,36,38,41,44].…”

Section: Introductionmentioning

confidence: 99%

Lost in the Digital Wild

Wang

et al. 2018

Proceedings of the 2nd International Workshop on Multimedia Privacy and Security

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This paper presents a new general framework of information hiding, in which the hidden information is embedded into a collection of activities conducted by selected human and computer entities (e.g., a number of online accounts of one or more online social networks) in a selected digital world. Different from other traditional schemes, where the hidden information is embedded into one or more selected or generated cover objects, in the new framework the hidden information is embedded in the fact that some particular digital activities with some particular attributes took place in some particular ways in the receiver-observable digital world.In the new framework the concept of "cover" almost disappears, or one can say that now the whole digital world selected becomes the cover. The new framework can find applications in both security (e.g., steganography) and non-security domains (e.g., gaming). For security applications we expect that the new framework calls for completely new steganalysis techniques, which are likely more complicated, less effective and less efficient than existing ones due to the need to monitor and analyze the whole digital world constantly and in real time. A proof-of-concept system was developed as a mobile app based on Twitter activities to demonstrate the information hiding framework works. We are developing a more hybrid system involving several online social networks.

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Hidden message in a deformation-based texture

Cited by 55 publications

References 30 publications

Recent Advances of Image Steganography With Generative Adversarial Networks

Recent Advances of Image Steganography With Generative Adversarial Networks

Generative Steganography by Sampling

Lost in the Digital Wild

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