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：摘要：图像深度隐写(Deep Steganography，简称"IDS")是利用深度学习技术将秘密图像隐匿地嵌入到封面图像中，生成容器图像的一种技术。然而，卷积神经网络(Convolutional Neural Networks，简称"CNNs")生成的容器图像容易受到攻击，从而破坏其高频部分。为解决这个问题，我们提出了一种新的方法，称为低频图像深度隐写术 (Low-frequency Image Deep Steganography，简称"LIDS")，允许在嵌入过程中进行频率分布操纵。LIDS从秘密图像中提取一个特征图，将其添加到封面图像中生成容器图像。由于容器图像不是直接由CNNs输出的，因此不包含高频成分。提取的特征图受到频率损失的调节，以确保其频率分布主要集中在低频域。为进一步提高鲁棒性，插入了一个攻击层以损坏容器图像，检索网络则从被损坏的容器图像中恢复出秘密图像。我们的实验表明，LIDS在保持高质量和高特异性的同时，优于当前最先进的方法的鲁棒性。通过避免高频成分并操纵嵌入特征图的频率分布，LIDS实现了对攻击的高度鲁棒性。