End-to-end Trained CNN Encode-Decoder Networks for Image Steganography

Rehman, Atique ur; Rahim, Rafia; Nadeem, Muhammad Shahroz; Hussain, Sibt ul

doi:10.48550/arxiv.1711.07201

Cited by 4 publications

(10 citation statements)

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“…The network structure comprises three components: a pre-processing network resizing the secret image to match the cover image's size, an encoder network combining the secret image and cover image to generate the stego image, and a decoder network facilitating the extraction of the secret image. Rehman et al [22] made improvements on this foundation, but both steganographic networks led to the issue of colour distortion. Zhang et al [23] addressed this by converting the RGB format of the steganography-performed image to YCrYb format.…”

Section: Image Steganographymentioning

confidence: 99%

“…Information-hiding encoder-decoder networks proposed by researchers such as Baluja [15] and Rehman et al [22] typically adopt an independent design for encoder and decoder networks. This approach results in the irreversibility of the secret information encoding and decoding processes, consequently diminishing the success rate of secret information restoration.…”

Section: Chaos Mapping Enhanced Image Steganography Network (Chase)mentioning

confidence: 99%

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A High-Capacity and High-Security Image Steganography Network Based on Chaotic Mapping and Generative Adversarial Networks

Huo,

Chen,

Wei

et al. 2024

Applied Sciences

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With the enhancement of information volume, people are not satisfied with transmitting only a single secret image at a time but chase to hide multiple secret images in a single picture; however, the large-capacity steganographic scale can easily lead to the degradation of the quality of the image, which attracts the attention of eavesdroppers. In this paper, we propose a Chaotic mapping-enHanced imAge Steganography nEtwork (CHASE), which pioneers to hide colour images in grey images and reduces the difference between the container image and the cover image through the image permutation method, so as to enhance the security of the steganography. The method demonstrates excellent steganalysis resistance in experiments and introduces Generative Adversarial Networks (GANs) to improve the image fidelity in large-capacity steganographic scales. The fusion of chaotic mapping and GAN optimisation enables the steganographic network to simultaneously balance security and image quality. The experimental results show that CHASE can keep the secret image with good invisibility under large-capacity steganographic scales, and at the same time, it can reveal the secret image with high fidelity, and its steganalysis-resistant capability is much better than other state-of-the-art methods.

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Section: Image Steganographymentioning

confidence: 99%

Section: Chaos Mapping Enhanced Image Steganography Network (Chase)mentioning

confidence: 99%

A High-Capacity and High-Security Image Steganography Network Based on Chaotic Mapping and Generative Adversarial Networks

Huo,

Chen,

Wei

et al. 2024

Applied Sciences

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“…In [78], Rehman et al proposed a CNN based encoderdecoder architecture to hide a secret image in a cover image to form stego image and also to extract the hidden secret embedded in the stego image. The encoder network takes two images (i.e.…”

Section: Appendix Amentioning

confidence: 99%

Digital image steganalysis: A survey on paradigm shift from machine learning to deep learning based techniques

Arivazhagan¹,

Ezhilarasan²,

Jebarani³

et al. 2020

IET Image Processing

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Steganography, a branch of data hiding techniques aims to hide confidential information within any digital media by obscuring the existence of hidden information. On the contrary, steganalysis aims to detect steganography. With the advent of powerful steganographic algorithms, the process of cracking them became very challenging. Traditional steganalysis following machine learning principle employs a two-step process, with first process extracting highly sophisticated features capable of discriminating hidden message from original data and second process classifying the input as innocent or guilty. In recent years, deep learning which has its roots in artificial neural networks emerged as a brilliant alternative for many computer vision tasks. A review of recent research works in deep learning based digital image steganalysis is presented here. The paradigm shift from machine learning approaches to employing more promising deep learning architectures, observed with the current research community and hence in literature has been presented here in chronological order. Deep learning can unify the two-step process into a single process by giving the ability for machine to learn end-to-end by itself. The use of Convolutional Neural Networks to perform steganalysis in spatial or transform or combination of both domains has effectively lowered the detection error rates.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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“…Consider the optimal characters of the disentangled latent code w , these two networks also use w as the input. Our noise optimization network is mainly inspired by PGGAN and ResNet [33][34][35] Two simple designing rules are given to optimize the injecting noise: First, we introduc the progressive mechanism to generate the noise with the same size as the different reso lution image. Second, the disentangled latent code w is indirectly utilized to form th secure noise by shortcut connection.…”

mentioning

confidence: 99%

“…Second, the disentangled latent code w is indirectly utilized to form th secure noise by shortcut connection. The progressive model is formed by three blocks in Our noise optimization network is mainly inspired by PGGAN and ResNet [33][34][35]. Two simple designing rules are given to optimize the injecting noise: First, we introduce the progressive mechanism to generate the noise with the same size as the different resolution image.…”

mentioning

confidence: 99%

Secure Steganographic Cover Generation via a Noise-Optimization Stacked StyleGAN2

Zhou

et al. 2023

Symmetry

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Recently, the style-based generative adversarial network StyleGAN2 yields state-of-art performance on unconditional high-quality image synthesis. However, from the perspective of steganography, the image security is not guaranteed during the image synthesis. Relying on the optimal properties of StyleGAN2, this paper proposes a noise-optimization stacked StyleGAN2 named NOStyle to generate the secure and high-quality cover (image used for data hiding). In our proposed scheme, we decompose the image synthesis into two stages with symmetrical mode. In stage-I, StyleGAN2 is preserved to generate a high-quality benchmark image. In the stage-II generator, based on the progressive mechanism and shortcut connection, we design a noise secure optimization network by which the different-scale stochastic variation (noise map) is automatically adjusted according to the results of the stage-II discriminator. After injecting the stochastic variation into different resolutions of the synthesis network, the stage-II generator obtains an intermediate image. For the symmetrical stage-II discriminator, we combine the image secure loss and fidelity loss to construct the noise loss which is used to evaluate the difference between two images generated by the stage-I generator and stage-II generator. Taking the outputs of stage-II discriminator as inputs, by iteration, the stage-II generator finally creates the optimal image. Extensive experiments show that the generated image is not only secure but high quality. Moreover, we make a conclusion that the security of the generated image is inverse proportion to the fidelity.

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End-to-end Trained CNN Encode-Decoder Networks for Image Steganography

Cited by 4 publications

References 0 publications

A High-Capacity and High-Security Image Steganography Network Based on Chaotic Mapping and Generative Adversarial Networks

A High-Capacity and High-Security Image Steganography Network Based on Chaotic Mapping and Generative Adversarial Networks

Digital image steganalysis: A survey on paradigm shift from machine learning to deep learning based techniques

Secure Steganographic Cover Generation via a Noise-Optimization Stacked StyleGAN2

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