A noise-constrained lightweight high-quality image hiding method based on invertible neural networks
Abstract Image steganography is an important branch of steganography that aims to hide secret information in a cover image, making it difficult to be detected by the human eye, while allowing only authorized parties to recover the secret information. Most existing image hiding methods have a large n...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Springer
2025-06-01
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| Series: | Complex & Intelligent Systems |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s40747-025-01943-4 |
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| Summary: | Abstract Image steganography is an important branch of steganography that aims to hide secret information in a cover image, making it difficult to be detected by the human eye, while allowing only authorized parties to recover the secret information. Most existing image hiding methods have a large number of parameters, complex structures, and significant storage requirements. Although some methods have fewer parameters, they often produce low-quality results. To address these issues, this paper proposes a noise-constrained lightweight high-quality image hiding method based on invertible neural networks, called NCL-Net. The method introduces a novel multi-scale dense connection structure (MSDCS), an efficient interaction block (EIB), and a noise-constrained loss, achieving high capacity, high quality, lightweight design, and strong generalization in image hiding tasks. Extensive experiments demonstrate that the proposed method outperforms state-of-the-art methods in terms of generated image quality, model storage usage, lightweight design, and practicality. |
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| ISSN: | 2199-4536 2198-6053 |