Image Hash Combining Quaternion Laguerre Moments and Three-Dimensional Structures
Currently,several algorithms used in image hashing can only handle grayscale images.In this study,a hashing algorithm based on quaternion Laguerre moments and a three-dimensional energy structure is proposed to improve the range of application and performance of the image hashing algorithm,particularly its robustness against rotation attacks.First,the input color image is preprocessed,multiscale fusion is performed,and the Laguerre moment coefficients are extracted from the fused image as the global features of the image.The energy information of the fused image is used to establish a model in the YCbCr color space,and the angle between the energy peak and valley points connected with the horizontal plane at different viewpoints in the three-dimensional(3D)model is selected as the local structure feature.The features with rotational invariance are extracted by the positions of the near and far points on the specific points and each contour of the 3D model.Finally,the global and 3D structural features are combined,quantized,and encrypted to generate hash sequences.Finally,the global and 3D structural features are combined to quantify and encrypt the generated hash sequences.The results show that the subject operating characteristic curve exhibits a correct acceptance rate of 0.999 2 when the error reception rate is 0.A Hash sequence length of 120 bit possesses optimal compactness,and the average computation time reaches 0.097 9 s.In copy detection experiments,the algorithm performs multiple extraction experiments with an average check-all rate,and the average check rate and accuracy rate of the algorithm for multiple extraction experiments are higher than 95.83％.
万方数据
维普
