Optimization analysis for image-based steganography using generative adversarial networks

Aldrin Wilfred Arokiasamy; Władysław Skarbek

doi:10.1117/12.2536836

6 November 2019 Optimization analysis for image-based steganography using generative adversarial networks

Aldrin Wilfred Arokiasamy, Władysław Skarbek

Proceedings Volume 11176, Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments 2019; 111760U (2019) https://doi.org/10.1117/12.2536836
Event: Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments 2019, 2019, Wilga, Poland

Abstract

Steganography is a method of hiding secret information within non-secret information. For the purpose of steganography, a lot of works based on convolutional neural network(CNN) were framed recent years and they showed the improvement of deep learning particularly in the field of hiding information. The major key factors that were kept in the account by those works include enhancing the capacity, invisibility, and security. In this research, a work based on steganography via generative adversarial networks was utilized to increase the invisibility and security, thus extracting that same secret image at the receiver side precisely. The focus of this research was to select the best suitable optimizer for the image based Steganography. Here, Stochastic Gradient Descent (SGD) and Adaptive Momentum (Adam) were compared and from the investigation, it was concluded that Adam optimizer performs better in handling the model to improve the hiding and revealing ability.

Citation Download Citation

Aldrin Wilfred Arokiasamy and Władysław Skarbek "Optimization analysis for image-based steganography using generative adversarial networks", Proc. SPIE 11176, Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments 2019, 111760U (6 November 2019); https://doi.org/10.1117/12.2536836

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KEYWORDS

Steganography

Image analysis

Image enhancement

Image processing

Computer programming

Stochastic processes

Network security

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