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.

翻译：深度学习技术在数据隐藏领域的应用显著推动了安全通信与身份验证领域的发展。通过将信息嵌入到音频、视频或图像等抗噪声信号中，数字水印和隐写术技术能够保护敏感知识产权并实现机密通信，确保所嵌入信息仅限授权方访问。本综述系统梳理了近年来用于数据隐藏的深度学习技术发展，根据模型架构和噪声注入方法进行系统分类，全面总结了训练这些数据隐藏模型所用的目标函数、评估指标和数据集。此外，本文还探讨并提出了将数字水印与隐写术在软件工程领域统一起来以增强安全性、降低风险的潜在未来研究方向。该贡献有助于构建更可信的数字世界，推动负责任的人工智能发展。