Secure communication through reliable S-box design: A proposed approach using coset graphs and matrix operations

Razaq, Abdul; Alhamzi, Ghaliah; Abbas, S. M.; Ahmad, Musheer; Razzaque, Asima

doi:10.1016/j.heliyon.2023.e15902

Cited by 18 publications

(3 citation statements)

References 40 publications

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“…In contrast to cryptography, which primarily focuses on ensuring message confidentiality, digital watermarking places greater emphasis on copyright protection and tracing [1,2]. Classical watermarking involves the covert embedding of a watermark (a sequence of data) within media files, allowing for the extraction of this watermark even after data distribution or manipulation, enabling the identification of data sources or copyright ownership [3].…”

Section: Introductionmentioning

confidence: 99%

ZWNet: A Deep-Learning-Powered Zero-Watermarking Scheme with High Robustness and Discriminability for Images

Li,

Sun,

Wang

et al. 2024

Applied Sciences

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In order to safeguard image copyrights, zero-watermarking technology extracts robust features and generates watermarks without altering the original image. Traditional zero-watermarking methods rely on handcrafted feature descriptors to enhance their performance. With the advancement of deep learning, this paper introduces “ZWNet”, an end-to-end zero-watermarking scheme that obviates the necessity for specialized knowledge in image features and is exclusively composed of artificial neural networks. The architecture of ZWNet synergistically incorporates ConvNeXt and LK-PAN to augment the extraction of local features while accounting for the global context. A key aspect of ZWNet is its watermark block, as the network head part, which fulfills functions such as feature optimization, identifier output, encryption, and copyright fusion. The training strategy addresses the challenge of simultaneously enhancing robustness and discriminability by producing the same identifier for attacked images and distinct identifiers for different images. Experimental validation of ZWNet’s performance has been conducted, demonstrating its robustness with the normalized coefficient of the zero-watermark consistently exceeding 0.97 against rotation, noise, crop, and blur attacks. Regarding discriminability, the Hamming distance of the generated watermarks exceeds 88 for images with the same copyright but different content. Furthermore, the efficiency of watermark generation is affirmed, with an average processing time of 96 ms. These experimental results substantiate the superiority of the proposed scheme over existing zero-watermarking methods.

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Section: Introductionmentioning

confidence: 99%

ZWNet: A Deep-Learning-Powered Zero-Watermarking Scheme with High Robustness and Discriminability for Images

Li,

Sun,

Wang

et al. 2024

Applied Sciences

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“…This challenging environment necessitates a reevaluation of existing cryptographic countermeasures. The scientific community is called upon to not only improve but to fundamentally transform security approaches [23]. The motivation for this transformation is grounded in a critical understanding: isolated, even potent, modern cryptographic techniques may fall short against multifaceted attacks.…”

Section: Introductionmentioning

confidence: 99%

A Holistic Secure Communication Mechanism Using a Multilayered Cryptographic Protocol to Enhanced Security

Wang,

Tabassum

2024

CMC

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In an era characterized by digital pervasiveness and rapidly expanding datasets, ensuring the integrity and reliability of information is paramount. As cyber threats evolve in complexity, traditional cryptographic methods face increasingly sophisticated challenges. This article initiates an exploration into these challenges, focusing on key exchanges (encompassing their variety and subtleties), scalability, and the time metrics associated with various cryptographic processes. We propose a novel cryptographic approach underpinned by theoretical frameworks and practical engineering. Central to this approach is a thorough analysis of the interplay between Confidentiality and Integrity, foundational pillars of information security. Our method employs a phased strategy, beginning with a detailed examination of traditional cryptographic processes, including Elliptic Curve Diffie-Hellman (ECDH) key exchanges. We also delve into encrypt/decrypt paradigms, signature generation modes, and the hashes used for Message Authentication Codes (MACs). Each process is rigorously evaluated for performance and reliability. To gain a comprehensive understanding, a meticulously designed simulation was conducted, revealing the strengths and potential improvement areas of various techniques. Notably, our cryptographic protocol achieved a confidentiality metric of 9.13 in comprehensive simulation runs, marking a significant advancement over existing methods. Furthermore, with integrity metrics at 9.35, the protocol's resilience is further affirmed. These metrics, derived from stringent testing, underscore the protocol's efficacy in enhancing data security.

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“…Tempered-type fractional derivatives in discrete time were proposed, and the encryption was discussed in [31]. Motivated by the investigations performed in the recent literature towards the field of image encryption [32][33][34][35][36][37][38], this article aims to contribute the following.…”

Section: Introductionmentioning

confidence: 99%

A Novel Fractional Sine Chaotic Map and Its Application to Image Encryption and Watermarking

2023

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This article addresses the telecommunications industry’s priority of ensuring information security during the transition to next-generation networks. It proposes an image encryption system that combines watermarking techniques and a discrete fractional sine chaotic map. The authors also incorporate the principles of blockchain to enhance the security of transmitted and received image data. The proposed system utilizes a newly developed sine chaotic map with a fractional difference operator, exhibiting long-term chaotic dynamics. The complexity of this map is demonstrated by comparing it with three other fractional chaotic maps from existing literature, using bifurcation diagrams and the largest Lyapunov exponent. The authors also show the map’s sensitivity to changes in initial conditions through time-series diagrams. To encrypt images, the authors suggest a method involving watermarking of two secret images and encryption based on blockchain technology. The cover image is watermarked with the two hidden images using discrete wavelet transformations. Then, the image pixels undergo diffusion using a chaotic matrix generated from the discrete fractional sine chaotic map. This encryption process aims to protect the image data and make it resistant to unauthorized access. To evaluate the algorithm, the authors perform statistical analysis and critical sensitivity analysis to examine its characteristics. They also analyse different attacks to assess the algorithm’s ability to resist such threats and maintain image quality after decryption. The results demonstrate that the proposed algorithm effectively defends against attacks and ensures image security.

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Secure communication through reliable S-box design: A proposed approach using coset graphs and matrix operations

Cited by 18 publications

References 40 publications

ZWNet: A Deep-Learning-Powered Zero-Watermarking Scheme with High Robustness and Discriminability for Images

ZWNet: A Deep-Learning-Powered Zero-Watermarking Scheme with High Robustness and Discriminability for Images

A Holistic Secure Communication Mechanism Using a Multilayered Cryptographic Protocol to Enhanced Security

A Novel Fractional Sine Chaotic Map and Its Application to Image Encryption and Watermarking

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