Image hashing by SDQ-CSLBP

TLDR: The proposed method of SDQ-CSLBP extract texture feature using CSLBP with standard deviation as weight factor with experimental results show that the proposed method is robust against content preserving manipulation and sensitive to content changing and structural tampering.

Abstract: Approach for image hashing is to use powerful feature descriptor which captures essence of an image. Applications of image hashing lies in the area of content authentication, structural tampering detection, retrieval and recognition. Hashing is a compact summarized information of an image. Center Symmetric Local Binary Pattern (CSLBP) is one of the powerful texture feature descriptor which captures the smallest amount of change. Using CSLBP, appressed hash code can be obtained for an image. If CSLBP feature is weighted by a boost factor, it will enhance success rate of an image hashing. The proposed method of SDQ-CSLBP extract texture feature using CSLBP with standard deviation as weight factor. Standard deviation which represents local contrast is also a powerful descriptor. Resultant histogram of CSLBP is of 16 bin for each block of an image. Further it can be compressed to 8 bin by using the flipped difference concept. Without a weight factor, compressed CSLBP has low discrimination power. Experimental results show that the proposed method is robust against content preserving manipulation and sensitive to content changing and structural tampering.