Perceptual hashing for color images

Currently, the reported image hashing schemes cannot perform satisfactorily in some aspects, such as rotation robustness. To this end, a perceptual color image hashing scheme is proposed. First, the original image is normalized and smoothed by Gaussian low-pass filter. The obtained secondary image is divided into a series of ring-ribbons with different radii and the same number of pixels. Then, textural and color features are extracted in local and global manners. Specifically, local textural features are extracted on luminance values of the ring-ribbons using quadtree decomposition, and global textural features are extracted by gray level co-occurrence matrix. Local color features of significant corner points are extracted on outer boundaries of ring-ribbons through color vector angles, and global color features are extracted by low-order moments. The extracted features are concatenated after quantization and permutation to generate the final hash. Receiver operating characteristic curves verified the effectiveness of our scheme, including robustness, discrimination, and security, which can be effectively applied in content authentication and tampering detection.