Compressed Image Hashing using Minimum Magnitude CSLBP

Abstract

Image hashing allows compression, enhancement or other signal processing operations on digital images which are usually acceptable manipulations. Whereas, 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 proposed a novel image hashing algorithm for authentication which is more robust against various kind of attacks. In proposed approach, a short hash code is obtained by using minimum magnitude Center Symmetric Local Binary Pattern (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 Center Symmetric Local Binary Pattern (CSLBP). The discrimination power of hashing is increased by weight factor during CSLBP histogram construction. The generated histogram is compressed to 1/4 of the original histogram by minimum magnitude CSLBP. The proposed method, has a twofold advantage, first is a small length and second is acceptable discrimination power. Experimental results are demonstrated by hamming distance, TPR, FPR and ROC curves. Therefore the proposed method successfully does a fair classification of content preserving and content changing images.