Perceptual Image Hashing Based on Canny Operator and Tensor for Copy-Move Forgery Detection

Abstract Copy-move is a common image forgery operation, which copies and moves a block of an image from one position to another place. Image hashing refers to extracting a unique number sequence from the image by using various image features. In practical application, image hashing is used to replace the image itself, which effectively reduces the cost of image storage and computational complexity. In this paper, we propose a novel image hash extraction scheme: constructing image hashing by combining local feature based on Canny operator and global feature based on tensor. In addition, instead of using the traditional correlation coefficient or Hamming distance, a novel method is proposed to calculate the hash distances. A large number of experiments have proved that our image hashing can achieve a better balance between robustness and discrimination with a shorter hash length. What’s more, we can directly locate the forgery areas from the hashing for copy-move forged images.