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Invisible Steganography via Generative Adversarial Networks (1807.08571v3)

Published 23 Jul 2018 in cs.MM and cs.CV

Abstract: Nowadays, there are plenty of works introducing convolutional neural networks (CNNs) to the steganalysis and exceeding conventional steganalysis algorithms. These works have shown the improving potential of deep learning in information hiding domain. There are also several works based on deep learning to do image steganography, but these works still have problems in capacity, invisibility and security. In this paper, we propose a novel CNN architecture named as \isgan to conceal a secret gray image into a color cover image on the sender side and exactly extract the secret image out on the receiver side. There are three contributions in our work: (i) we improve the invisibility by hiding the secret image only in the Y channel of the cover image; (ii) We introduce the generative adversarial networks to strengthen the security by minimizing the divergence between the empirical probability distributions of stego images and natural images. (iii) In order to associate with the human visual system better, we construct a mixed loss function which is more appropriate for steganography to generate more realistic stego images and reveal out more better secret images. Experiment results show that ISGAN can achieve start-of-art performances on LFW, Pascal VOC2012 and ImageNet datasets.

Citations (180)

Summary

  • The paper introduces ISGAN, a convolutional neural network that embeds secret images in the Y channel to achieve superior invisibility.
  • The method employs adversarial training via GANs to minimize statistical detectability, substantially boosting security against steganalysis.
  • A mixed loss function combining SSIM, MS-SSIM, and MSE is used to optimize image quality, demonstrating state-of-the-art performance on multiple datasets.

Invisible Steganography via Generative Adversarial Networks

The paper "Invisible Steganography via Generative Adversarial Networks" presents an innovative approach to enhance the field of image steganography using deep learning techniques. Specifically, the authors propose a novel convolutional neural network architecture named ISGAN, which employs Generative Adversarial Networks (GANs) to address inherent issues related to capacity, invisibility, and security in steganography. This essay outlines the significant aspects of the paper, the methodologies employed, results obtained, and potential implications for future research in AI.

Key Contributions and Methodology

The paper highlights three essential contributions made by the authors:

  1. Improved Invisibility: By embedding the secret image exclusively in the Y channel of the cover image, the authors mitigate the distortion that typically affects the color information in stego images. The focus on the Y channel, which predominantly contains luminance information rather than color, allows for stronger invisibility of the stego image.
  2. Enhanced Security: Through the application of Generative Adversarial Networks, the method aims to reduce the statistical detectability of stego images by minimizing the divergence between the probability distributions of stego and natural images. The adversarial component of GANs aids in fortifying the steganography method's resistance against steganalysis.
  3. Mixed Loss Function: The authors introduce a mixed loss function incorporating Structure Similarity Index (SSIM) and its variant, Multi-Scale SSIM (MS-SSIM), alongside traditional Mean Square Error (MSE). This combination is designed to optimize the model based on a human visual system perspective, aiming to produce more naturally similar stego images while accurately revealing the secret images.

Results

The ISGAN model exhibits state-of-the-art performance on multiple datasets, including LFW, PASCAL-VOC12, and ImageNet. The results indicate that ISGAN achieves superior invisibility and security compared to existing methods such as Atique's model and other traditional steganography techniques. The numerical findings showcase notable improvements in metrics like SSIM between stego and cover images, asserting the strength of ISGAN in maintaining visual fidelity and reducing detectability.

Implications and Future Directions

The implications of this research are multifaceted, offering advancements in practical applications like secure communication, digital watermarking, and copyright protection. The deployment of GANs within steganography presents a promising avenue for enhancing security and efficiency in information hiding practices.

The paper opens several pathways for future research. Continued refinement of adversarial training methods could yield even more robust steganography techniques. Additionally, exploring the integration of GANs with other deep learning architectures might further elevate capabilities, potentially leading to steganographic methods that are resilient to loss in data transmission.

Moreover, examining the adaptation of ISGAN to various domains beyond image steganography, such as audio and video, might broaden the scope and utility of GAN-based approaches in information security.

Conclusion

In conclusion, the Invisible Steganography via Generative Adversarial Networks paper proposes compelling solutions to long-standing challenges in steganography. By leveraging the strengths of GANs and introducing innovative loss functions, the authors offer a model with promising improvements in invisibility and security. This research underscores the potential for deep learning to transform traditional steganography practices and sets a foundation for further development and exploration in AI-driven security solutions.