Compressed Image Hashing using Minimum Magnitude CSLBP

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PATIL V.; Sarode T.

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Journal Paper

Paper Information

Journal: JOURNAL OF ARTIFICIAL INTELLIGENCE AND DATA MINING Year:2019 | Volume:7 | Issue:2 Page(s): 287-297

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Title

Compressed Image Hashing using Minimum Magnitude CSLBP

Author(s)

PATIL V. | Sarode T. | Issue Writer Certificate

Keywords

Authentication

CSLBP

LBP

Hashing

Histogram

Tampering

Abstract

Image Hashing allows compression, enhancement or other signal processing operations on digital images that are usually acceptable manipulations. Cryptographic hash functions are very sensitive to even single bit changes in image. Image Hashing is a sum of important quality features in quantized form. In this paper, we propose a novel image Hashing algorithm for Authentication, which is more robust against various kinds of attacks. In the proposed approach, a short hash code is obtained using a minimum magnitude Center Symmetric Local Binary Pattern (LBP/fa?page=1&sort=1&ftyp=all&fgrp=all&fyrs=all" target="_blank">CSLBP). The desirable discrimination power of image hash is maintained by modified Local Binary Pattern (LBP) based edge weight factor generated from gradient image. The proposed Hashing method extracts texture features using the LBP/fa?page=1&sort=1&ftyp=all&fgrp=all&fyrs=all" target="_blank">CSLBP. The discrimination power of Hashing is increased by weight factor during the LBP/fa?page=1&sort=1&ftyp=all&fgrp=all&fyrs=all" target="_blank">CSLBP Histogram construction. The generated Histogram is compressed to 1/4 of the original Histogram by a minimum magnitude of LBP/fa?page=1&sort=1&ftyp=all&fgrp=all&fyrs=all" target="_blank">CSLBP. The proposed method, has a two-fold advantage; first, it has small length, and second, it has an acceptable discrimination power. The experimental results are demonstrated by the hamming distance and the TPR, FPR, and ROC curves. Therefore, the proposed method successfully does a fair classification of content preserving and content changing images.

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APA: Copy

PATIL, V., & Sarode, T.. (2019). Compressed Image Hashing using Minimum Magnitude CSLBP. JOURNAL OF ARTIFICIAL INTELLIGENCE AND DATA MINING, 7(2), 287-297. SID. https://sid.ir/paper/725522/en

Vancouver: Copy

PATIL V., Sarode T.. Compressed Image Hashing using Minimum Magnitude CSLBP. JOURNAL OF ARTIFICIAL INTELLIGENCE AND DATA MINING[Internet]. 2019;7(2):287-297. Available from: https://sid.ir/paper/725522/en

IEEE: Copy

V. PATIL, and T. Sarode, “Compressed Image Hashing using Minimum Magnitude CSLBP,” JOURNAL OF ARTIFICIAL INTELLIGENCE AND DATA MINING, vol. 7, no. 2, pp. 287–297, 2019, [Online]. Available: https://sid.ir/paper/725522/en

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