Extensive research has been conducted on image steganography and watermarking algorithms, owing to their crucial rules in secret data transmission, copyright protection, and traceability. Despite promising results and numerous surveys proposed in the literature, there is still a lack of comprehensive analysis dedicated to deep learning-based image steganography and watermarking algorithms. In this paper, we focus on investigating three important aspects: neural networks, structure models, and training strategies. Our review covers the vast literature in this field. Furthermore, we provide a comprehensive statistical analysis from diverse perspectives, including models, loss functions, platforms, datasets, and attacks. Moreover, we conduct in a thorough comparative analysis and evaluation of existing representative algorithms, assessing their effectiveness within the context of deep learning. Finally, the challenges and potential research directions in the domain of deep-learning image steganography and watermarking algorithms are discussed to facilitate future research.

中文翻译：

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基于学习的图像隐写术和水印：一项调查

由于图像隐写术和水印算法在秘密数据传输、版权保护和可追溯性方面的关键规则，人们对图像隐写术和水印算法进行了广泛的研究。尽管文献中提出了有希望的结果和大量的调查，但仍然缺乏专门针对基于深度学习的图像隐写术和水印算法的全面分析。在本文中，我们重点研究三个重要方面：神经网络、结构模型和训练策略。我们的评论涵盖了该领域的大量文献。此外，我们从不同角度提供全面的统计分析，包括模型、损失函数、平台、数据集和攻击。此外，我们对现有代表性算法进行彻底的比较分析和评估，评估它们在深度学习背景下的有效性。最后，讨论了深度学习图像隐写术和水印算法领域的挑战和潜在研究方向，以促进未来的研究。