Steganography is the art of hiding secret data into the cover media for covert communication. In recent years, more and more deep neural network (DNN)-based steganographic schemes are proposed to train steganographic networks for secret embedding and recovery, which are shown to be promising. Compared with the handcrafted steganographic tools, steganographic networks tend to be large in size. It raises concerns on how to imperceptibly and effectively transmit these networks to the sender and receiver to facilitate the covert communication. To address this issue, we propose in this paper a Purified and Unified Steganographic Network (PUSNet). It performs an ordinary machine learning task in a purified network, which could be triggered into steganographic networks for secret embedding or recovery using different keys. We formulate the construction of the PUSNet into a sparse weight filling problem to flexibly switch between the purified and steganographic networks. We further instantiate our PUSNet as an image denoising network with two steganographic networks concealed for secret image embedding and recovery. Comprehensive experiments demonstrate that our PUSNet achieves good performance on secret image embedding, secret image recovery, and image denoising in a single architecture. It is also shown to be capable of imperceptibly carrying the steganographic networks in a purified network. Code is available at \url{https://github.com/albblgb/PUSNet}

翻译：隐写术是一种将秘密数据隐藏于载体媒体中以实现隐蔽通信的艺术。近年来，越来越多的基于深度神经网络（DNN）的隐写方案被提出，用于训练隐写网络以实现秘密嵌入与恢复，这些方案展现出良好的应用前景。相较于手工设计的隐写工具，隐写网络往往规模较大，这引发了如何将这类网络以隐蔽且高效的方式传输给发送方与接收方以支持隐蔽通信的问题。为解决这一难题，本文提出了一种纯净统一隐写网络（Purified and Unified Steganographic Network, PUSNet）。该网络在一个纯净网络中执行常规机器学习任务，并可通过不同密钥触发其转变为秘密嵌入或恢复的隐写网络。我们将PUSNet的构建形式化为稀疏权重填充问题，以实现纯净网络与隐写网络之间的灵活切换。进一步地，我们将PUSNet实例化为一个图像去噪网络，其中隐藏了两个用于秘密图像嵌入与恢复的隐写网络。全面实验表明，我们的PUSNet在单一架构下能够同时实现秘密图像嵌入、秘密图像恢复和图像去噪的良好性能。实验结果还显示，该网络能够以不可察觉的方式在纯净网络中承载隐写网络。代码已开源在\url{https://github.com/albblgb/PUSNet}。