Robust image hashing based on statistical features for copy detection

Image hashing is one of the emergent novel approaches used extensively in the field of image forensics apart from finding its place in many of the latest techniques of the area of image indexing, image retrieval etc. Image hashing is basically used to identify the duplicate copies of the original images. Most of the image hashing algorithms has their limitations in getting the desirable performance against a particular image processing attack i.e. rotation. In this paper, we have proposed image hashing technique dominantly based on statistical features of the image which is robust to almost all kind of image processing attacks including rotation. In our proposed algorithm input image is normalized by using resizing, Gaussian filtering, color space conversion from RGB image to YCbCr and only Y component is taken for hash generation. Radon transform is then applied to the preprocessed image to produce 2-D Radon coefficients. 1-D DCT is then applied to the Radon coefficients to produce column-wise DCT coefficients. Lastly first AC coefficient from each column are taken to form the row-wise vector which is used to extract four statistical features, Mean, Standard Deviation, Kurtosis & Skewness. The extracted features form the final feature vector which is used for image identification. Many experiments have conducted to compare the proposed technique with the state-of-the-art techniques and the results shows that proposed hashing is robust to normal digital operations apart from giving excellent result against rotation.