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Image deep steganography (IDS) is a technique that utilizes deep learning toembed a secret image invisibly into a cover image to generate a containerimage. 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 ImageDeep Steganography (LIDS) that allows frequency distribution manipulation inthe embedding process. LIDS extracts a feature map from the secret image andadds it to the cover image to yield the container image. The container image isnot directly output by the CNNs, and thus, it does not contain high-frequencyartifacts. The extracted feature map is regulated by a frequency loss to ensurethat its frequency distribution mainly concentrates on the low-frequencydomain. To further enhance robustness, an attack layer is inserted to damagethe container image. The retrieval network then retrieves a recovered secretimage from a damaged container image. Our experiments demonstrate that LIDSoutperforms state-of-the-art methods in terms of robustness, while maintaininghigh fidelity and specificity. By avoiding high-frequency artifacts andmanipulating the frequency distribution of the embedded feature map, LIDSachieves improved robustness against attacks that distort the high-frequencycomponents of container images.

Low-frequency Image Deep Steganography: Manipulate the Frequency Distribution to Hide Secrets with Tenacious Robustness