Improved Pixel and Bit Confusion-Diffusion Based on Mixed Chaos and Hash Operation for Image Encryption

Andono, Pulung Nurtantio; Setiadi, De Rosal Ignatius Moses

doi:10.1109/access.2022.3218886

Cited by 25 publications

(12 citation statements)

References 56 publications

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“…Usage of logistic map is also proven to offer better encryption performance [11]. An effective image encryption method can be a modified Lorenz chaotic system utilizing Arnold transformation with differential evolution to provide robust feature for encryption of image [12,13]. In terms of security and better visual clarity the proposed techniques outperform all the other techniques.…”

Section: Existing Research Contribution Towards Image Securitymentioning

confidence: 99%

A Faster and Robust Artificial Neural Network Based Image Encryption Technique With Improved SSIM

Panigrahy,

Maniyath,

Sathiyanarayanan

et al. 2024

IEEE Access

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A robust image encryption process is still one of the most challenging tasks in image security owing to massive degree and sensitivity nature of information in the form of pixels. The hurdles include greater computational difficulty, information loss during encryption, universality, applicability of the approach, and less scalability. Many image encryption methods existing in literature merely encrypt a portion of the data. Therefore, we propose a robust, dynamic, and sophisticated technique to enhance the encryption process to make it difficult for an attacker to gain unauthorized access to the pixel data. The proposed system uses a novel analytical research methodology through dynamically harnessing the potential of neural network that offers better forward and backward secrecy, dynamic control, and automatic management unlike any existing system. The encryption procedure comprises of two levels, first level is confusion-permutation of input image and second level is diffusion by Bit XOR operation for secure transmission and storage of images. Finally, the encrypted image is used as a target for training the Artificial Neural Network (ANN) model. ANN trained values are used for final level of encryption to develop a Neural Network (NN)-based cryptosystem, where the crypto analyst or the cracker need to know the number of adaptive iterations and the final weights for the encryption and decryption systems to crack the system which offers higher degree of resiliency towards potential threats. Results and security analysis show that our algorithm has good encryption effect, ability of resisting exhaustive attack, statistical attack, and differential attack. The system performance after implementing the proposed method is compared with existing methods present in literature with respect to processing time and Structural Similarity Index Measure (SSIM). Our proposed method offers significant reduction in encryption time and is approximately 10-15% faster than others with SSIM of 0.002165, close to zero after encryption. It also successfully balances the image quality with higher image security and lower computational complexity.

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Section: Existing Research Contribution Towards Image Securitymentioning

confidence: 99%

A Faster and Robust Artificial Neural Network Based Image Encryption Technique With Improved SSIM

Panigrahy,

Maniyath,

Sathiyanarayanan

et al. 2024

IEEE Access

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“…The encryption involves six stages to encrypt the digital images. In [47], Winarno et al proposed a study introducing a combination of Zigzag, Hilbert, and Morton patterns for enhancing digital image security. The keystream, generated from improved logistic and 6D hyperchaotic maps, contributes highly random and nonlinear keystreams.…”

Section: Related Workmentioning

confidence: 99%

Innovative Cybersecurity for Enhanced Data Protection: An Extended Bit-Plane Extraction and Chaotic Permutation-Diffusion Approach in Information Security

Rehman,

Shafique,

Khan

et al. 2024

IEEE Access

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In the era of big data, protecting digital images from cyberattacks during network transmission is of utmost importance. While various image encryption algorithms have been developed, some remain vulnerable to specific cyber threats. This paper presents an enhanced version of the image encryption algorithm based on bit-plane extraction (BPCPD) to address its vulnerability to chosen-plaintext attacks. The proposed cryptographic system encompasses three primary phases. The initial phase involves bit-plane extraction from the plaintext image and the generation of random sequences and a random image using multiple chaotic maps, such as the chaotic Arnold map and the chaotic CAT map. The second phase is dedicated to permutation operations, which comprise three sub-phases: multi-layer permutation, multi-round permutation, and recursive permutation. In the third phase, diffusion is introduced to the permuted image through pixel substitution, coupled with XOR operations performed on the respective bit-planes of the random image. To gauge the efficiency of the proposed encryption scheme, a range of experimental analyses are conducted, including histogram analysis, contrast assessment, entropy measurement, correlation analysis, encryption quality assessment, and investigations into noise attacks and occlusion attacks. The results of these experimental analyses, in comparison to an existing encryption scheme, demonstrate that the proposed framework surpasses both BPCPD and other existing encryption schemes in various aspects of performance.

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“…Diffusion, on the other hand, distributes data in its initial form efficiently and uniformly throughout the entire ciphered image. Encryption methods achieve diffusion and confusion [4,5] through dense substitution and permutation of each pixel. Substitution is performed by changing the image pixel values to other values.…”

Section: Introductionmentioning

confidence: 99%

Enhanced Color Image Encryption Utilizing a Novel Vigenere Method with Pseudorandom Affine Functions

El Bourakkadi,

Chemlal,

Tabti

et al. 2024

ATAIML

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In the realm of digital image security, this study presents an innovative encryption methodology for color images, significantly advancing the traditional Vigenere cipher through the integration of two extensive pseudorandom substitution matrices. These matrices are derived from chaotic maps widely recognized for their cryptographic utility, specifically the logistic map and the skew tent map, chosen for their straightforward implementation capabilities in encryption systems and their high sensitivity to initial conditions. The process commences with the vectorization of the original image and the computation of initial values to alter the starting pixel's value, thereby initiating the encryption sequence. A novel aspect of this method is the introduction of a Vigenere mechanism that employs dynamic pseudorandom affine functions at the pixel level, enhancing the cipher's robustness. Subsequently, a comprehensive permutation strategy is applied to bolster the vector's integrity and elevate the temporal complexity against potential cryptographic attacks. Through simulations conducted on a varied collection of images, encompassing different sizes and formats, the proposed encryption technique demonstrates formidable resilience against both brute-force and differential statistical attacks, thereby affirming its efficacy and security in safeguarding digital imagery.

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Improved Pixel and Bit Confusion-Diffusion Based on Mixed Chaos and Hash Operation for Image Encryption

Cited by 25 publications

References 56 publications

A Faster and Robust Artificial Neural Network Based Image Encryption Technique With Improved SSIM

A Faster and Robust Artificial Neural Network Based Image Encryption Technique With Improved SSIM

Innovative Cybersecurity for Enhanced Data Protection: An Extended Bit-Plane Extraction and Chaotic Permutation-Diffusion Approach in Information Security

Enhanced Color Image Encryption Utilizing a Novel Vigenere Method with Pseudorandom Affine Functions

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