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

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Titel: Enhancing steganography capacity through multi-stage generator model in generative adversarial network based image concealment.
Autoren: Sultan, Bisma, Wani, Mohd. Arif
Quelle: Journal of Electronic Imaging; May/Jun2024, Vol. 33 Issue 3, p33026-033026-18, 1p
Schlagwörter: GENERATIVE adversarial networks, DEEP learning, NETWORK performance
Abstract: 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. [ABSTRACT FROM AUTHOR]
Copyright of Journal of Electronic Imaging is the property of SPIE - International Society of Optical Engineering and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Enhancing steganography capacity through multi-stage generator model in generative adversarial network based image concealment.
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  Data: <searchLink fieldCode="AR" term="%22Sultan%2C+Bisma%22">Sultan, Bisma</searchLink><br /><searchLink fieldCode="AR" term="%22Wani%2C+Mohd%2E+Arif%22">Wani, Mohd. Arif</searchLink>
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  Data: Journal of Electronic Imaging; May/Jun2024, Vol. 33 Issue 3, p33026-033026-18, 1p
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– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: 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. [ABSTRACT FROM AUTHOR]
– Name: Abstract
  Label:
  Group: Ab
  Data: <i>Copyright of Journal of Electronic Imaging is the property of SPIE - International Society of Optical Engineering and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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      – Type: doi
        Value: 10.1117/1.JEI.33.3.033026
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      – Code: eng
        Text: English
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        StartPage: 33026
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      – SubjectFull: GENERATIVE adversarial networks
        Type: general
      – SubjectFull: DEEP learning
        Type: general
      – SubjectFull: NETWORK performance
        Type: general
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      – TitleFull: Enhancing steganography capacity through multi-stage generator model in generative adversarial network based image concealment.
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              Text: May/Jun2024
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