基于分形覆盖和混沌DNA组合加密的图像隐写术

Q1 Decision Sciences Annals of Data Science Pub Date : 2022-10-22 DOI:10.1007/s40745-022-00457-x
Asha Durafe, Vinod Patidar
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引用次数: 0

摘要

为了满足安全传输数字图像的需要,我们开发了一种满足隐写术所有重要前提条件的算法。通过结合分形封面图像的显著特征、使用标准混沌图的双层加密技术和 DNA 混沌加密技术,以及 DWT-SVD 嵌入技术,本作品针对鲁棒性、更好的感知质量和高有效载荷容量等关键方面,构建了一种盲彩色图像隐写术算法。利用 DWT-SVD 嵌入方法,分形覆盖图像被用来隐藏 DNA 混沌加密彩色图像。二维标准混沌图在很大的参数范围内表现出鲁棒性混沌,被用来生成具有加密品质的伪随机数序列。该方法的核心创新之一是采用两层混沌加密方法生成 DNA 加密密文图像,最后利用 DWT-SVD 变换域技术将其嵌入分形覆盖图像中,从而抵御假阳性攻击。全面的统计安全测试和标准评估基准表明,这种高效而简单的混合隐写术算法具有很强的鲁棒性和可持续性,可抵御移除、几何、图像增强和直方图攻击,提供更好的感知图像质量,同时还有助于提高提取图像的感知质量。
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Image Steganography Using Fractal Cover and Combined Chaos-DNA Based Encryption

To address the need for secure digital image transmission an algorithm that fulfils all prominent prerequisites of a steganography technique is developed. By incorporating the salient features of fractal cover images, dual-layer encryption using the standard chaotic map and DNA-hyperchaotic cryptography along with DWT-SVD embedding, key aspects like robustness, better perceptual quality and high payload capacity are targeted to build a blind colour image steganography algorithm in this work. A fractal cover image is used to hide a DNA-chaotic encrypted colour image using DWT-SVD embedding method. A two-dimensional standard chaotic map, which exhibits robust chaos for a very large range of parameter, is used to generate the pseudo-random number sequences of cryptographic qualities. One of the core novelty of the proposed method is the 2 layers chaotic encryption method to generate the DNA encrypted secret image which is finally embedded in a fractal cover image using DWT-SVD transform domain technique capable of withstanding the false positive attack. The comprehensive statistical security tests and the standard evaluation benchmarks depict that this efficient yet simple hybrid steganography algorithm is highly robust as well as sustainable against removal, geometrical, image enhancement and histogram attacks, offers better perceptual image quality and also contributes high perceptual quality of the extracted image.

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来源期刊
Annals of Data Science
Annals of Data Science Decision Sciences-Statistics, Probability and Uncertainty
CiteScore
6.50
自引率
0.00%
发文量
93
期刊介绍: Annals of Data Science (ADS) publishes cutting-edge research findings, experimental results and case studies of data science. Although Data Science is regarded as an interdisciplinary field of using mathematics, statistics, databases, data mining, high-performance computing, knowledge management and virtualization to discover knowledge from Big Data, it should have its own scientific contents, such as axioms, laws and rules, which are fundamentally important for experts in different fields to explore their own interests from Big Data. ADS encourages contributors to address such challenging problems at this exchange platform. At present, how to discover knowledge from heterogeneous data under Big Data environment needs to be addressed.     ADS is a series of volumes edited by either the editorial office or guest editors. Guest editors will be responsible for call-for-papers and the review process for high-quality contributions in their volumes.
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