A new framework for analyzing color models with generative adversarial networks for improved steganography

Sultan, Bisma; ArifWani, M.

doi:10.1007/s11042-023-14348-7

Cited by 8 publications

(4 citation statements)

References 33 publications

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“…The authors in Ref. 25 demonstrate that, for deep learning steganography, secret data embedded in the XYZ image format offers better invisibility and security at varying capacities than RGB images. In Ref.…”

Section: Related Workmentioning

confidence: 99%

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Enhancing steganography capacity through multi-stage generator model in generative adversarial network based image concealment

Sultan,

Wani

2024

J. Electron. Imag.

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Traditional steganography algorithms use procedures created by human experts to conceal the secret message inside a cover medium. Generative adversarial networks (GANs) have recently been used to automate this process. However, GAN based steganography has some limitations. The capacity of these models is limited. By increasing the steganography capacity, security is decreased, and distortion is increased. The performance of the extractor network also decreases with increasing the steganography capacity. In this work, an approach for developing a generator model for image steganography is proposed. The approach involves building a generator model, called the late embedding generator model, in two stages. The first stage of the generator model uses only the flattened cover image, and second stage uses a secret message and the first stage's output to generate the stego image. Furthermore, a dual-training strategy is employed to train the generator network: the first stage focuses on learning fundamental image features through a reconstruction loss, and the second stage is trained with three loss terms, including an adversarial loss, to incorporate the secret message. The proposed approach demonstrates that hiding data only in the deeper layers of the generator network boosts capacity without requiring complex architectures, reducing computational storage requirements. The efficacy of the proposed approach is evaluated by varying the depth of these two stages, resulting in four generator models. A comprehensive set of experiments was performed on the CelebA dataset, which contains more than 200,000 samples. The results show that the late embedding model performs better than the state-of-the-art models. Also, it increases the steganography capacity to more than four times compared with the existing GAN-based steganography methods. The extracted payload achieves an accuracy of 99.98%, with the extractor model successfully decoding the secret message.

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Section: Related Workmentioning

confidence: 99%

“…The authors in Ref. 25 illustrate that embedding secret data in the XYZ image format offers improved invisibility and security in deep learning steganography compared with RGB images. Another contribution by Ref.…”

Section: Introductionmentioning

confidence: 99%

Enhancing steganography capacity through multi-stage generator model in generative adversarial network based image concealment

Sultan,

Wani

2024

J. Electron. Imag.

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“…Sultan and ArifWani 29 presented a novel framework for enhancing the security and imperceptibility of steganographic images. To conceal sensitive information within cover photos, the approach combined generative adversarial networks (GANs) and color models.…”

Section: Related Workmentioning

confidence: 99%

Approach to secret content tagging on color images with a Sudoku-based scrambling principle

et al. 2023

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At present, there has been much focus on information hiding because it acts as an alternative to secure communication. Secret content is embodied in an image using various image embodiment techniques, with the least significant bit (LSB) substitution being one of the preferred strategies. Asserting the originality and quality of the content embodiment is equally challenging. A scrambling mechanism using a Sudoku scheme is applied on all red, green, and blue layer blocks of the image. The image layers are devised into blocks, and over the blocks unique scrambling procedures are applied to acquire the scrambled image. The scrambled image is then exposed to the confidential stream embodiment using the traditional LSB substitution scheme by following the chess game-based queen tour traversal pattern for locating the pixels. After the embedding process, the scrambled image is fragmented into blocks, and the exact descrambling of the blocks is performed to get back the image, which visually resembles the original image called the stego image. The stego image gets split into blocks at the receiver end to apply exact scrambling on the corresponding blocks, and using the queen tour pattern, the secret stream is to be extracted. As a result of the experiments, it is found that the projected approach is unlikely to be very predictable and unique in its patterns.

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“…It can hide up to 50% of the stego-audio from a size perspective and can conceal 22-37 bits of data in a two-second stego-audio from a semantic perspective. Sultan et al, (2023) examined several widely used colour models, such as YCrCb, YDbDr, HSV, CIE-XYZ, YIQ, HED, and YUV. These colour models are taken into consideration for improving steganography with GANs.…”

Section: Generative Adversarial Neural Network For Image Steganographymentioning

confidence: 99%

A Review Study on various Image Security Techniques and Emerging trends for Visual Data Protection

Dr. K. Rajeswari, Mrs. Dipali Ghatge

2023

tjjpt

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In the rapidly evolving digital age, the proliferation of visual data and images has led to increased concerns about their security and protection. This paper reviews various image security techniques and emerging trends aimed at safeguarding visual data in a digital landscape. The research explores the evolution of image security measures and their adaptation to address contemporary challenges in data protection. This study introduces the stage by elucidating the multifaceted nature of image security, emphasizing its significance in preserving the confidentiality and integrity of visual data. The complex landscape of image security threats is examined, highlighting the challenges faced in safeguarding visual data from various vulnerabilities. This paper delves into the need and significance of image security in the digital age, presenting a compelling case for the adoption of robust security measures. It explores the risks associated with image theft and tampering in the online world and underscores the essential role of image encryption and authentication methods in countering these threats. The study then delves into the realm of encryption techniques, offering insights into both symmetric and asymmetric cryptographic algorithms designed to enhance image security on the internet. The research extends its focus to secure data hiding techniques, such as reversible data hiding in encrypted images, histogram shifting-based data hiding, and high-embedding capacity methods employing optimization algorithms. The paper also explores digital watermarking as a means of image authentication and copyright protection, along with image tamper detection and self-recovery mechanisms in watermarking schemes. Additionally, this review assesses the impact of emerging trends, including machine learning, Blockchain, and AI-based solutions, on the security of visual data, such as deep neural networks, generative adversarial neural networks, and their role in image steganography, forensics, and privacy protection. Consequently, by providing an in-depth analysis of the current state of image security and future directions, this study contributes to the understanding of the critical role played by image security in the digital age and its implications for data protection and privacy.

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A new framework for analyzing color models with generative adversarial networks for improved steganography

Cited by 8 publications

References 33 publications

Enhancing steganography capacity through multi-stage generator model in generative adversarial network based image concealment

Enhancing steganography capacity through multi-stage generator model in generative adversarial network based image concealment

Approach to secret content tagging on color images with a Sudoku-based scrambling principle

A Review Study on various Image Security Techniques and Emerging trends for Visual Data Protection

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