Learning-based image steganography and watermarking: A survey

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. • Comprehensive review of deep learning-based image steganography and watermarking. • In-depth analysis of neural networks , structure models, and training strategies. • Extensive statistical analysis of current models, datasets, attack, and platform.