Deep learning is regarded as a promising solution for reversible steganography. There is an accelerating trend of representing a reversible steo-system by monolithic neural networks, which bypass intermediate operations in traditional pipelines of reversible steganography. This end-to-end paradigm, however, suffers from imperfect reversibility. By contrast, the modular paradigm that incorporates neural networks into modules of traditional pipelines can stably guarantee reversibility with mathematical explainability. Prediction-error modulation is a well-established reversible steganography pipeline for digital images. It consists of a predictive analytics module and a reversible coding module. Given that reversibility is governed independently by the coding module, we narrow our focus to the incorporation of neural networks into the analytics module, which serves the purpose of predicting pixel intensities and a pivotal role in determining capacity and imperceptibility. The objective of this study is to evaluate the impacts of different training configurations upon predictive accuracy of neural networks and provide practical insights. In particular, we investigate how different initialisation strategies for input images may affect the learning process and how different training strategies for dual-layer prediction respond to the problem of distributional shift. Furthermore, we compare steganographic performance of various model architectures with different loss functions.

翻译：深度学习被视为可逆隐写领域极具前景的解决方案。当前研究趋势倾向于用单一神经网络构建可逆隐写系统，以绕过传统可逆隐写流程中的中间操作。然而，这种端到端范式存在可逆性不完善的问题。相比之下，将神经网络集成到传统流程模块中的模块化范式，能够以数学可解释的方式稳定保证可逆性。预测误差调制是面向数字图像的成熟可逆隐写流程，包含预测分析模块与可逆编码模块。鉴于可逆性由编码模块独立控制，我们将研究重点聚焦于神经网络在分析模块中的集成，该模块承担像素强度预测的核心功能，对隐写容量与不可感知性起决定性作用。本研究旨在评估不同训练配置对神经网络预测精度的影响，并提炼实践启示。具体而言，我们探讨了输入图像初始化策略对学习过程的作用机制，以及双层预测中不同训练策略如何应对分布偏移问题。此外，对比分析了采用不同损失函数的多种模型架构的隐写性能表现。