Steganography using a 3-player game

Yedroudj, Mehdi; Comby, Frédéric; Chaumont, Marc

doi:10.1016/j.jvcir.2020.102910

Cited by 28 publications

(16 citation statements)

References 13 publications

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“…After the development of GANs, a research spurring paper appeared in late 2016, featuring two neural networks learning a symmetric key encryption system in the presence of an adversary [13]. Several follow up works performed a study on the security [18], ported it to steganography [20], [21] or improved its security [19]. More details and a security analysis are summarized in Table 1 and explained in Section IV.…”

Section: Neural Network Based Cryptographymentioning

confidence: 99%

“…Besides encryption, different researchers in two recent papers [20], [21] pushed the idea of the model proposed by Abadi et al [13] in order to build a steganography model based on neural networks.…”

Section: G Steganographymentioning

confidence: 99%

“…In this paper, we aim to survey the recent progress on neural networks based cryptography, how it evolved since the late 90s, explain the model proposed by Abadi et al [13] and how it learns to encrypt a communication. We will also see how other researchers [20], [21] ported this model to steganography. Finally, we will evaluate the security of the model proposed by Abadi et al [13] based on the security analysis done by Zhou et al [18].…”

Section: Introductionmentioning

confidence: 95%

“…Following Abadi and Andersen's work [13], a flow of research appeared in order to study the security of their model (e.g. [18]), as well as extend it to an assumed perfectly secure protocol [19], and many more [20]- [23].…”

Section: Introductionmentioning

confidence: 99%

“…Other works will also be discussed. We will then discuss the GANs based encryption technique [13] and how the neural networks learn to encrypt the communication as well as some follow up works [20], [21], [23].…”

Section: Introductionmentioning

confidence: 99%

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Neural Networks-Based Cryptography: A Survey

Meraouche¹,

Dutta

Tan

et al. 2021

IEEE Access

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A current trend of research focuses on artificial intelligence based cryptography which although proposed almost thirty years ago could not attract much attention. Abadi and Anderson's work on adversarial cryptography in 2016 rejuvenated the research area which now focuses in building neural networks that are able to learn cryptography using the idea from Generative Adversarial Networks (GANs). In this paper, we survey the most prominent research works that cover neural networks based cryptography from two main periods. The first period covers the oldest models that have been proposed shortly after 2000 and the second period covers the more recent models that have been proposed since 2016. We first discuss the implementation of the systems from the earlier era and the attacks mounted on them. After that, we focus on post 2016 era where more advanced techniques are utilized that rely on GANs in which neural networks compete with each other in order to achieve a goal e.g. learning to encrypt a communication. Finally, we discuss security analysis performed on adversarial cryptography models.INDEX TERMS cryptography, deep learning, neural networks, generative adversarial networks

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Section: Neural Network Based Cryptographymentioning

confidence: 99%

Section: G Steganographymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 95%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Neural Networks-Based Cryptography: A Survey

Meraouche¹,

Dutta

Tan

et al. 2021

IEEE Access

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Deep learning based image steganography: A review

Wani

Sultan

2022

WIREs Data Min & Knowl

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A review of the deep learning based image steganography techniques is presented in this paper. For completeness, the recent traditional steganography techniques are also discussed briefly. The three key parameters (security, embedding capacity, and invisibility) for measuring the quality of an image steganographic technique are described. Various steganography techniques, with emphasis on the above three key parameters, are reviewed. The steganography techniques are classified here into three main categories: Traditional, Hybrid, and fully Deep Learning. The hybrid techniques are further divided into three sub‐categories: Cover Generation, Distortion Learning, and Adversarial Embedding. The fully Deep Learning techniques, based on the nature of the input, are further divided into three sub‐categories: GAN Embedding, Embedding Less, and Category Label. The main ideas of the important deep learning based steganography techniques are described. The strong and weak features of these techniques are outlined. The results reported by researchers on benchmark data sets CelebA, Bossbase, PASCAL‐VOC12, CIFAR‐100, ImageNet, and USC‐SIPI are used to evaluate the performance of various steganography techniques. Analysis of the results shows that there is scope for new suitable deep learning architectures that can improve the capacity and invisibility of image steganography. This article is categorized under: Technologies > Computational Intelligence Technologies > Machine Learning Technologies > Artificial Intelligence

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