Generative Reversible Data Hiding by Image-to-Image Translation via GANs

Zhang, Zhuo; Fu, Guohong; Di, Fuqiang; Li, Changlong; Liu, Jia

doi:10.1155/2019/4932782

Cited by 25 publications

(18 citation statements)

References 26 publications

(24 reference statements)

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“…Zhang et al [25] proposed a generative reversible data hiding technique (GRDH) based on multiple GAN models, which differs from the previous RDH model. The cover image in this paper was generated by noise vector through the deep convolutional generative adversarial network (DCGAN) [26], in which the noise vector was mapped from secret data.…”

Section: Generative Reversible Data Hiding By Image To Image Translation Via Gansmentioning

confidence: 99%

“…By this method of transformation, each group of secret data can be mapped to a certain interval of noise vector values, so there is a deviation tolerance to ensure the correct rate of secret data retrieval. Zhang et al [25] trained different GAN models for image transformation in Cy-cleGAN, similar to image encryption and decryption in RDH. The secret data were mapped to the noise vector to generate the image by DCGAN so that the secret data were embedded into the image.…”

Section: 𝑟 = 𝑟𝑎𝑛𝑑𝑜𝑚mentioning

confidence: 99%

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Applying GMEI-GAN to Generate Meaningful Encrypted Images in Reversible Data Hiding Techniques

Tsai

et al. 2021

Symmetry

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With the rapid development of information technology, the transmission of information has become convenient. In order to prevent the leakage of information, information security should be valued. Therefore, the data hiding technique has become a popular solution. The reversible data hiding technique (RDH) in particular uses symmetric encoding and decoding algorithms to embed the data into the cover carrier. Not only can the secret data be transmitted without being detected and retrieved completely, but the cover carrier also can be recovered without distortion. Moreover, the encryption technique can protect the carrier and the hidden data. However, the encrypted carrier is a form of ciphertext, which has a strong probability to attract the attention of potential attackers. Thus, this paper uses the generative adversarial networks (GAN) to generate meaningful encrypted images for RDH. A four-stage network architecture is designed for the experiment, including the hiding network, the encryption/decryption network, the extractor, and the recovery network. In the hiding network, the secret data are embedded into the cover image through residual learning. In the encryption/decryption network, the cover image is encrypted into a meaningful image, called the marked image, through GMEI-GAN, and then the marked image is restored to the decrypted image via the same architecture. In the extractor, 100% of the secret data are extracted through the residual learning framework, same as the hiding network. Lastly, in the recovery network, the cover image is reconstructed with the decrypted image and the retrieved secret data through the convolutional neural network. The experimental results show that using the PSNR/SSIM as the criteria, the stego image reaches 45.09 dB/0.9936 and the marked image achieves 38.57 dB/0.9654. The proposed method not only increases the embedding capacity but also maintains high image quality in the stego images and marked images.

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Section: Generative Reversible Data Hiding By Image To Image Translation Via Gansmentioning

confidence: 99%

Section: 𝑟 = 𝑟𝑎𝑛𝑑𝑜𝑚mentioning

confidence: 99%

Applying GMEI-GAN to Generate Meaningful Encrypted Images in Reversible Data Hiding Techniques

Tsai

et al. 2021

Symmetry

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“…It is hard for traditional steganography to resist steganalysis based on deep learning because it is difficult to maintain all the features during data hiding. Therefore, scholars have introduced deep learning into the design of steganography [17]- [21]. In [20], Hayes et al proposed a tripartite adversarial steganographic training scheme.…”

Section: Introductionmentioning

confidence: 99%

Steganographic Secret Sharing With GAN-Based Face Synthesis and Morphing for Trustworthy Authentication in IoT

2021

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Trust and security are fundamental to the successful adoption of the Internet of Things (IoT). This paper proposes a secure message authentication scheme based on steganographic secret sharing for building trust in IoT systems. In our scheme, the message is split and distributed to two participants by a dealer, and it can be revealed only when the two authorized participants grant their consents. Neither of the participants can disclose the message without the consent of the other. To avoid malicious cyberattacks in IoT communications, each share of message is concealed in the form of a human face image, referred to as the shadow image, via a generative adversarial network (GAN). For each participant, a convolutional neural network (CNN) is trained to extract the share of message from the shadow image generated with the participant's key. Distortions and alterations to the shadow images may occur during the transmission from the dealer to the participant. As a tamper-evident design, each shadow image is morphed with the participant's source image under the participant's customized morphing parameter. Cheater detection is also crucial for involved participants to identify fake shadow images during the secret retrieval process. As a cheating countermeasure, the shadow image for one participant is morphed with that for the other and then morphed further with the given source image. The proposed scheme enables multi-factor authentication in the sense that the message is protected by the keys, source images, and morphing parameters of two participants.

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“…In this scheme, the cover image can be translated into the embedding change probability, so the embedding capacity has been improved. Zhang et al [16] proposed a generative reversible data hiding (GRDH) scheme, which can implement the task of secret information deliver by using image-to-image translation. Islam et al [17] proposed a novel image steganography technique based on most significant bits (MSB) of image pixels.…”

Section: Introductionmentioning

confidence: 99%

A Novel Grayscale Image Steganography Scheme Based on Chaos Encryption and Generative Adversarial Networks

Wang

et al. 2020

IEEE Access

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This paper proposes a novel grayscale image steganography scheme that is capable of hiding an encrypted secret image into a cover image of the same size. The secret image is encrypted using the chaos encryption technology before being hidden. The encrypted image and cover image are then transformed into the steg image by a convolutional neural network (CNN). Also, a generative adversarial network (GAN) is adopted to produce a more realistic steg image, whose appearance is difficult to distinguish from the cover image. The steganography scheme is composed of three CNNs which are regarded as hiding network, discriminative network, and extracting network, respectively. Additionally, a new weight allocation mechanism is introduced to ensure the balanced training procedure of hiding-extracting networks. The obtained experimental results show that the proposed scheme not only solved the problems of secret information leakage and color distortion of the current mainstream methods but also maintain high embedding capacity. Compared with other methods, the average values of the PSNR and SSIM of our steg images can reach 0.987 and 42.3, respectively. And the quality of the reconstructed secret images is also advantageous. INDEX TERMSGrayscale image steganography; Chaos encryption technology; Convolutional neural network (CNN); Generative adversarial network (GAN)

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Generative Reversible Data Hiding by Image-to-Image Translation via GANs

Cited by 25 publications

References 26 publications

Applying GMEI-GAN to Generate Meaningful Encrypted Images in Reversible Data Hiding Techniques

Applying GMEI-GAN to Generate Meaningful Encrypted Images in Reversible Data Hiding Techniques

Steganographic Secret Sharing With GAN-Based Face Synthesis and Morphing for Trustworthy Authentication in IoT

A Novel Grayscale Image Steganography Scheme Based on Chaos Encryption and Generative Adversarial Networks

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