Data Hiding with Deep Learning: A Survey Unifying Digital Watermarking and Steganography

• URL: http://arxiv.org/abs/2107.09287v3
• Date: Wed, 19 Apr 2023 05:09:08 GMT
• Title: Data Hiding with Deep Learning: A Survey Unifying Digital Watermarking and Steganography
• Authors: Zihan Wang, Olivia Byrnes, Hu Wang, Ruoxi Sun, Congbo Ma, Huaming Chen, Qi Wu, Minhui Xue
• Abstract summary: Digital watermarking and steganography techniques can be used to protect sensitive intellectual property and enable confidential communication. Future research directions that unite digital watermarking and steganography on software engineering to enhance security and mitigate risks are suggested and deliberated.
• Score: 33.12806297686689
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The advancement of secure communication and identity verification fields has significantly increased through the use of deep learning techniques for data hiding. By embedding information into a noise-tolerant signal such as audio, video, or images, digital watermarking and steganography techniques can be used to protect sensitive intellectual property and enable confidential communication, ensuring that the information embedded is only accessible to authorized parties. This survey provides an overview of recent developments in deep learning techniques deployed for data hiding, categorized systematically according to model architectures and noise injection methods. The objective functions, evaluation metrics, and datasets used for training these data hiding models are comprehensively summarised. Additionally, potential future research directions that unite digital watermarking and steganography on software engineering to enhance security and mitigate risks are suggested and deliberated. This contribution furthers the creation of a more trustworthy digital world and advances Responsible AI.

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