Beyond traditional steganography: enhancing security and performance with spread spectrum image steganography

Kuznetsov, Oleksandr; Frontoni, Emanuele; Chernov, Kyrylo

doi:10.1007/s10489-024-05415-z

Cited by 2 publications

References 49 publications

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Enhancing Steganography Detection with AI: Fine-Tuning a Deep Residual Network for Spread Spectrum Image Steganography

Kuznetsov,

Frontoni,

Chernov

et al. 2024

Sensors

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This paper presents an extensive investigation into the application of artificial intelligence, specifically Convolutional Neural Networks (CNNs), in image steganography detection. We initially evaluated the state-of-the-art steganalysis model, SRNet, on various image steganography techniques, including WOW, HILL, S-UNIWARD, and the innovative Spread Spectrum Image Steganography (SSIS). We found SRNet’s performance on SSIS detection to be lower compared to other methods, prompting us to fine-tune the model using SSIS datasets. Subsequent experiments showed significant improvement in SSIS detection, albeit at the cost of minor performance degradation as to other techniques. Our findings underscore the potential and adaptability of AI-based steganalysis models. However, they also highlight the need for a delicate balance in model adaptation to maintain effectiveness across various steganography techniques. We suggest future research directions, including multi-task learning strategies and other machine learning techniques, to further improve the robustness and versatility of steganalysis models.

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Enhancing Steganography Detection with AI: Fine-Tuning a Deep Residual Network for Spread Spectrum Image Steganography

Kuznetsov,

Frontoni,

Chernov

et al. 2024

Sensors

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The Role of Deep Learning Innovations With CNNs and GANs in Steganography

Kaur,

Chowdhary

2024

Advances in Information Security, Privacy, and Ethics

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Steganography, the practice of concealing information within other non-secret data, has evolved significantly with the advent of digital technologies. Traditional methods often struggle with maintaining a balance between capacity, imperceptibility, and robustness. Recent advancements in deep learning offer promising enhancements to steganographic techniques, addressing these challenges more effectively. This chapter explores how deep learning approaches can enhance steganography, focusing on the utilization of neural networks for embedding and extracting hidden information. Deep learning models, particularly convolutional neural networks (CNNs) and generative adversarial networks (GANs), have shown superior capability in learning intricate patterns and representations. CNNs can be employed to optimize the feature selection process, ensuring that hidden information is embedded in less noticeable regions of the cover media, thereby improving imperceptibility.

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Beyond traditional steganography: enhancing security and performance with spread spectrum image steganography

Cited by 2 publications

References 49 publications

Enhancing Steganography Detection with AI: Fine-Tuning a Deep Residual Network for Spread Spectrum Image Steganography

Enhancing Steganography Detection with AI: Fine-Tuning a Deep Residual Network for Spread Spectrum Image Steganography

The Role of Deep Learning Innovations With CNNs and GANs in Steganography

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