Implementasi Kriptografi dengan Menggunakan Algoritma Arnold’s Cat Map dan Henon Map

Irawan, Candra; Rachmawanto, Eko Hari

doi:10.14710/jmasif.13.1.43312

Cited by 1 publication

(2 citation statements)

References 9 publications

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“…Prior research utilized ACM to randomize by permuting image pixels before encryption [21], [22]. After the tampering, the encryption process continued using the XOR operation with pseudorandom values generated from the Hénon map.…”

Section: Face Recognition Encryption Trendmentioning

confidence: 99%

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Confusion and Diffusion Techniques for Image Encryption Process Based on Chaos System

Magfirawaty Magfirawaty,

Ariska Allamanda,

Malika Ayunasari

et al. 2024

Jurnal Nasional Teknik Elektro dan Teknologi Informasi

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Face recognition uses biometric technologies to identify humans based on their facial characteristics. This method is commonly used to restrict information access control. The benefits of face recognition systems encompass their ease of use and security. The human face recognition process consists of face detection, face tracking, and face recognition. The process uses some algorithms: the Viola-Jones for face detection, the Kanade-Lucas-Tomasi (KLT) for face tracking, and the principal component analysis (PCA) for face recognition. Furthermore, this research proposed face recognition with an encryption process to protect the data stored in a database. The encryption process consists of two main processes: confusion and diffusion. The confusion process is randomizing the position of the original image pixels. This research utilized the Arnold’s cat map (ACM) for the confusion process, and the diffusion process was performed using the XOR operation with the key generated from the 1D chaos system. Three different 1D chaos systems, namely logistic map, Bernoulli map, and tent map, were compared to see which chaos system had the best encryption results. Tests were conducted by comparing various parameters on the three proposed 1D chaos systems, including correlation coefficient, histogram analysis, entropy value, number of pixel rate changes (NPCR), and unified average change intensity (UACI). Based on testing the image encryption results, the diffusion process utilizing the tent map produced the best image encryption compared to other chaotic systems.

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Section: Face Recognition Encryption Trendmentioning

confidence: 99%

“…Research compared the peak noise to signal ratio (PNSR) values of four methods: ACM, Hénon map, ACM + Hénon map, and Hénon map + ACM [21]. It found that the best PNSR value was the method with ACM + Hénon map.…”

Section: Face Recognition Encryption Trendmentioning

confidence: 99%