Image Encryption by Using ACGLML (eBook, PDF)
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Doctoral Thesis / Dissertation from the year 2021 in the subject Engineering - Computer Engineering, , language: English, abstract: In the present-day era of typical social media communications, it is vital that privacy is to be preserved due to attacks on multimedia data through various methods. It is well known that the encryption standards either lack in keyspace or poor encryption strength. Hence, it is proposed to use a novel method in which both keyspace and encryption are increased. Discrete cosine transforms (DCT) of the image are encrypted using a generalized logistic equation. Due to...
Doctoral Thesis / Dissertation from the year 2021 in the subject Engineering - Computer Engineering, , language: English, abstract: In the present-day era of typical social media communications, it is vital that privacy is to be preserved due to attacks on multimedia data through various methods. It is well known that the encryption standards either lack in keyspace or poor encryption strength. Hence, it is proposed to use a novel method in which both keyspace and encryption are increased. Discrete cosine transforms (DCT) of the image are encrypted using a generalized logistic equation. Due to this idea, both compression and encryption are done simultaneously. Before applying DCT, the image is shuffled using Arnold Cat Map. The proposed compression and encryption method is validated using several chaotic metrics such as; Bifurcation diagram, Mutual information, Kolmogorov Sinai Entropy density, Kolmogorov Sinai Entropy generality, Space-Amplitude diagram, Space-Time diagram. Intruders are discouraged through the improved metrics such as NPCR (Number of Pixels changing rate), UACI (Unified Averaged changed intensity), and mutual information among the lattices used as the key. In the same way, image reconstruction quality is improved and verified through the metrics such as PSNR (Peak Signal to noise ratio) and FOM (Figure of Merit). Compression performance is evaluated through the metric CR (Compression ratio). The performance is evaluated for both gray and color image databases under a noisy channel environment.
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