Image deep steganography (IDS) is a technique that utilizes deep learning to embed a secret image invisibly into a cover image to generate a container image. However, the container images generated by convolutional neural networks (CNNs) are vulnerable to attacks that distort their high-frequency components. To address this problem, we propose a novel method called Low-frequency Image Deep Steganography (LIDS) that allows frequency distribution manipulation in the embedding process. LIDS extracts a feature map from the secret image and adds it to the cover image to yield the container image. The container image is not directly output by the CNNs, and thus, it does not contain high-frequency artifacts. The extracted feature map is regulated by a frequency loss to ensure that its frequency distribution mainly concentrates on the low-frequency domain. To further enhance robustness, an attack layer is inserted to damage the container image. The retrieval network then retrieves a recovered secret image from a damaged container image. Our experiments demonstrate that LIDS outperforms state-of-the-art methods in terms of robustness, while maintaining high fidelity and specificity. By avoiding high-frequency artifacts and manipulating the frequency distribution of the embedded feature map, LIDS achieves improved robustness against attacks that distort the high-frequency components of container images.

翻译：图像深度隐写术（IDS）是一种利用深度学习将秘密图像不可见地嵌入载体图像以生成容器图像的技术。然而，卷积神经网络（CNN）生成的容器图像易受攻击扭曲其高频分量。为解决该问题，我们提出一种名为低频图像深度隐写术（LIDS）的新方法，允许在嵌入过程中操控频率分布。LIDS从秘密图像中提取特征图，并将其叠加到载体图像上以生成容器图像。容器图像并非由CNN直接输出，因此不含高频伪影。提取的特征图通过频率损失函数进行约束，确保其频率分布主要集中于低频域。为增强鲁棒性，插入攻击层对容器图像施加损伤，随后检索网络从受损容器图像中恢复出秘密图像。实验表明，LIDS在保持高保真度与特异性的同时，在鲁棒性上优于现有最优方法。通过规避高频伪影并操控嵌入特征图的频率分布，LIDS实现了对扭曲容器图像高频分量的攻击的鲁棒性提升。