Generative Steganography Network

• URL: http://arxiv.org/abs/2207.13867v2
• Date: Fri, 29 Jul 2022 05:09:59 GMT
• Title: Generative Steganography Network
• Authors: Ping Wei, Sheng Li, Xinpeng Zhang, Ge Luo, Zhenxing Qian, Qing Zhou
• Abstract summary: We propose an advanced generative steganography network (GSN) that can generate realistic stego images without using cover images. A module named secret block is designed delicately to conceal secret data in the feature maps during image generation.
• Score: 37.182458848616754
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Steganography usually modifies cover media to embed secret data. A new steganographic approach called generative steganography (GS) has emerged recently, in which stego images (images containing secret data) are generated from secret data directly without cover media. However, existing GS schemes are often criticized for their poor performances. In this paper, we propose an advanced generative steganography network (GSN) that can generate realistic stego images without using cover images, in which mutual information is firstly introduced in stego image generation. Our model contains four sub-networks, i.e., an image generator ($G$), a discriminator ($D$), a steganalyzer ($S$), and a data extractor ($E$). $D$ and $S$ act as two adversarial discriminators to ensure the visual and statistical imperceptibility of generated stego images. $E$ is to extract the hidden secret from generated stego images. The generator $G$ is flexibly constructed to synthesize either cover or stego images with different inputs. It facilitates covert communication by hiding the function of generating stego images in a normal image generator. A module named secret block is designed delicately to conceal secret data in the feature maps during image generation, with which high hiding capacity and image fidelity are achieved. In addition, a novel hierarchical gradient decay skill is developed to resist steganalysis detection. Experiments demonstrate the superiority of our work over existing methods.

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