Perceptual Hashing for Color Images Using Invariant Moments

Image hashing is a new technology in multimedia security. It maps visually identical images to the same or similar short strings called image hashes, and finds applications in image retrieval, image authentication, digital watermarking, image indexing, and image copy detection. This paper presents a perceptual hashing for color images. The input image in RGB color space is firstly converted into a normalized image by interpolation and filtering. Color space conversions from RGB to YCbCr and HSI are then performed. Next, invariant moments of each component of the above two color spaces are calculated. The image hash is finally obtained by concatenating the invariant moments of these components. Similarity between image hashes is evaluated by L2 norm. Experiments show that the proposed hashing is robust against normal digital processing, such as JPEG compression, watermark embedding, gamma correction, Gaussian low-pass filtering, adjustments of brightness and contrast, image scaling, and image rotation. Receiver operating characteristics (ROC) comparisons between the proposed hashing and singular value decompositions (SVD) based hashing, also called SVD-SVD hashing, presented by Kozat et al. at the 11th International Conference on Image Processing (ICIP'04) are conducted, and the results indicate that the proposed hashing shows better performances in robustness and discriminative capability than the SVD-SVD hashing.