engleski

Evaluating Robustness of Perceptual Image Hashing Algorithms

In this paper we evaluate the robustness of perceptual image hashing algorithms. The image hashing algorithms are often used for various objectives, such as images search and retrieval, finding similar images, finding duplicates and near-duplicates in a large collection of images, etc. In our research, we examine the image hashing algorithms for images identification on the Internet. Hence, our goal is to evaluate the most popular perceptual image hashing algorithms with the respect to ability to track and identify images on the Internet and popular social network sites. Our basic criteria for evaluation of hashing algorithms is robustness. We find a hashing algorithm robust if it can identify the original image upon visible modifications are performed, such as resizing, color and contrast change, text insertion, swirl etc. Also, we want a robust hashing algorithm to identify and track images once they get uploaded on popular social network sites such as Instagram, Facebook or Google+. To evaluate robustness of perceptual hashing algorithms, we prepared an image database and we performed various physical image modifications. To compare robustness of hashing algorithms, we computed Precision, Recall and F1 score for each competing algorithm. The obtained evaluation results strongly confirm that P-hash is the most robust perceptual hashing algorithm.

Perceptual Hashing, Image Identification, Robustness

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