Arnold Transformed Position Power First Mapping (AT-PPFM) for Secret Digital Image

Wilis, Kaswidjanti and Hidayatulah, Himawan and Afra, Oryza and Mangaras, Yanu F and Rifki, Indra Perwira (2019) Arnold Transformed Position Power First Mapping (AT-PPFM) for Secret Digital Image. International Conference on Science in Information Technology (ICSITech), - (-). pp. 241-245.

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Digital image data stored and exchanged in cloud storage can be secured using cryptographic and steganographic techniques. The information contained in a data is secured in order to avoid taking data or information from unauthorized parties. The encryption process is done by changing the original image data from what can be understood to be incomprehensible. Encryption uses Arnold Transformation Algorithm which randomizes the pixel of image data by using 4- bit Most Significant hidden images to then be inserted into the encrypted 4-bit Least Significant Cover. Peak Signal to Noise (PSNR) is used to compare image quality before and after extraction. This comparison is based on the test results of the mean error value or Mean Square Error (MSE) of the original image data and the resulting image insertion data. PSNR produced in this study is above the minimum standard value (40 dB), which is between 45.60 dB - 46.10 dB, and the resulting distortion value is very small (MSE> 2). By using a 4-bit insertion of the existing image data, the extraction results are not much different from the hidden image before insertion and the results can be identified. So that the use of Arnold Transform and Position Power First Mapping (PPFM) algorithms reduces distortion and differences as well as the possibility of data leakage from the resulting image data.

Item Type: Article
Uncontrolled Keywords: Arnold Transform, PPFM, Cryptography, Steganography
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Faculty of Engineering, Science and Mathematics > School of Electronics and Computer Science
Date Deposited: 27 Jul 2020 10:05
Last Modified: 27 Jul 2020 10:09

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