WGAN-GP Based Generative Coverless Information Hiding

Hesong An () and Junling Ren ()
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Hesong An: Beijing Information Science & Technology University
Junling Ren: Beijing Information Science & Technology University

A chapter in LISS 2024, 2025, pp 405-418 from Springer

Abstract: Abstract As a prominent research focus in steganographic communication, coverless information hiding aims to fundamentally resist steganography detection algorithms. However, current methods face several challenges, including reliance on large-scale image databases, limited hiding capacity, and poor quality of generated images, which significantly constrain their practical applicability. To address these issues, this paper proposes a generative coverless information hiding method based on WGAN-GP. First, the mapping relationship between secret information and attribute label codes is established. The sender then inputs the secret information along with real images into a generator to produce images with specific styles. These generated images are subsequently combined into a secret image containing the hidden secret and sent to the receiver. The receiver uses a discriminator to analyze the received image and extract the hidden information. Experimental results demonstrate that the proposed method does not require large-scale image databases and excels in terms of image quality, hiding capacity, robustness, and security, offering a more viable solution for practical applications.

Keywords: coverless information hiding; WGAN-GP; attribute label code; image generation (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/978-981-96-9697-0_32

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