Individualized image steganography method with Dynamic Separable Key and Adaptive Redundancy Anchor

IF 7.6 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Knowledge-Based Systems Pub Date : 2025-01-30 Epub Date: 2024-11-20 DOI:10.1016/j.knosys.2024.112729
Junchao Zhou, Yao Lu, Guangming Lu
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Abstract

Image steganography hides several secret images into a single cover image to produce a stego image. For transmission security, the stego image is visually indistinguishable from the cover image. Furthermore, for effective transmission of secret information, the receivers should recover the secret images with high quality. With the increasing steganography capacity, a stego image containing many secret images is transmitted through public channels. However, in the existing image steganography methods, all the secret images are usually revealed without quarantine among various recipients. This problem casts a threat to security in the recovery process. In order to overcome this issue, we propose the Individualized Image Steganography (IIS) Method with Dynamic Separable Key (DSK) and Adaptive Redundancy Anchor (ARA). Specifically, in the process of hiding secret images, the proposed DSK dynamically generates a global key and a local key and appropriately fuses them together. In the same batch of transmission, all recipients share the same global key, but each has a different local key. Only by matching both the global key and the local key simultaneously, can the secret image be restored by the specific receiver, which makes the secret image individualized for the target recipient. Additionally, in the process of revealing secret images, the proposed ARA learns the adaptive redundancy anchor for the inverse training to drive the input redundancy of revealing (backward) process and output redundancy of hiding (forward) process to be close. This achieves a better trade-off between the performances of hiding and revealing processes, and further enhances both the quality of restored secret images and stego images. Jointly using the DSK and ARA, a series of experiments have verified that our IIS method has achieved satisfactory performance improvements in extensive aspects. Code is available in https://github.com/Revive624/Individualized-Invertible-Steganography.
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基于动态可分离键和自适应冗余锚的个性化图像隐写方法
图像隐写术将多个秘密图像隐藏到单个封面图像中,以产生隐写图像。为了传输安全,隐写图像在视觉上与封面图像难以区分。此外,为了有效地传输秘密信息,接收者需要高质量地恢复秘密图像。随着隐写能力的提高,一幅包含许多秘密图像的隐写图像通过公开通道传输。然而,在现有的图像隐写方法中,所有的秘密图像通常在不同的接收者之间不加隔离地显示。这个问题对恢复过程的安全性造成了威胁。为了克服这一问题,我们提出了一种基于动态可分离密钥(DSK)和自适应冗余锚(ARA)的个性化图像隐写(IIS)方法。具体来说,在隐藏秘密图像的过程中,DSK动态生成一个全局密钥和一个本地密钥,并将它们适当融合在一起。在同一批传输中,所有接收方共享相同的全局密钥,但每个接收方拥有不同的本地密钥。只有同时匹配全局密钥和本地密钥,特定接收者才能恢复秘密图像,从而使秘密图像对目标接收者具有个性化。此外,在秘密图像的揭示过程中,本文提出的ARA学习自适应冗余锚进行逆训练,以驱动揭示(后向)过程的输入冗余和隐藏(前向)过程的输出冗余接近。这在隐藏和显示过程的性能之间实现了更好的权衡,进一步提高了秘密图像和隐写图像的恢复质量。结合DSK和ARA进行的一系列实验证明,我们的IIS方法在广泛的方面取得了令人满意的性能改进。代码可从https://github.com/Revive624/Individualized-Invertible-Steganography获得。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Knowledge-Based Systems
Knowledge-Based Systems 工程技术-计算机:人工智能
CiteScore
14.80
自引率
12.50%
发文量
1245
审稿时长
7.8 months
期刊介绍: Knowledge-Based Systems, an international and interdisciplinary journal in artificial intelligence, publishes original, innovative, and creative research results in the field. It focuses on knowledge-based and other artificial intelligence techniques-based systems. The journal aims to support human prediction and decision-making through data science and computation techniques, provide a balanced coverage of theory and practical study, and encourage the development and implementation of knowledge-based intelligence models, methods, systems, and software tools. Applications in business, government, education, engineering, and healthcare are emphasized.
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