“…In conventional image hashing methods, image hash generation is a robust feature compression process without any learning stage. It includes (1) invariant feature transform based methods, such as Wavelet transform [1], Radon transform [2], Fourier-Mellin transform [3], DCT transform [4], and QFT transform [5], which aim to extract robust features from transform domains; (2) local feature points based methods, such as SIFT [6] and end-stopped wavelet [7], which take advantages of the invariant local feature under some content preserving image processing attacks; (3) dimension reduction based methods, such as singular value decomposition (SVD) [8], nonnegative matrix factorization (NMF) [9], and Fast Johnson-Lindenstrauss transform (FJLT) [10], which embed the low level features of the high dimensional space into lower dimension; (4) statistics features based methods, such as the robust image hashing with ring partition and invariant vector distance [11]. Moreover, Wang et al [12] propose a perceptual image hashing method by combining image block based features and key-point-based features.…”