Low-frequency Image Deep Steganography: Manipulate the Frequency Distribution to Hide Secrets with Tenacious Robustness. (arXiv:2303.13713v1 [cs.CR])

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 …

address attacks called cnns container container images deep learning distribution generated hide high ids images low manipulation networks neural networks novel problem process robustness secret secrets steganography vulnerable