A DIFFERENCE PERCEPTUAL HASHING ALGORITHM FOR MEDICAL VOLUME DATA AGAINST LOCAL NONLINEAR GEOMETRIC ATTACKS

Traditional image hashing methods can often only resist traditional global geometric attacks and cannot resist local nonlinear geometric attacks. This has a huge impact on the protection, identification and authentication of three-dimensional medical volume data. In response to these problems, a new difference perceptual hashing algorithm for medical volume data is proposed in this paper. It uses the difference between adjacent elements in each column of the volume data feature matrix to generate a hashing sequence. Experiments show that it has a good ability to resist local nonlinear geometric attacks.With the progress of communication technology, particularly the wide application of Internet, more and more medical images are transmitted in the public network [1-2]. How to protect the patient's personal information, medical image retrieval, identification and authentication, become more and more urgent [3-4]. The research of perceptual hashing is based on image watermarking technology, and also refers to the basic theory of traditional cryptography hashing and multimedia authentication [5-6]. It has become a hot spot in the related research fields of multimedia processing and security [7]. Perceptual hashing can transform multimedia data into shorter bit sequences. Perceptual hashing provides a reliable technical guarantee for the protection, identification and authentication of multimedia digital content. Perceptual hashing can be applied to medical images [8]. As the medical imaging equipment advance, most medical images used in hospitals are three-dimensional medical volume data, so it is of great significance to study the perceptual hashing of medical volume data. However, in practical applications, there is usually a type of geometric attacks, which belong to local nonlinear geometric attacks. At present, there are few perceptual algorithms that can resist local nonlinear geometric attacks, so it is of great significance to study perceptual hashing algorithms against local nonlinear geometric attacks. In order to solve this problem, a new difference hashing algorithm is proposed to resist local nonlinear geometric attacks. Experimental results prove that the difference hashing algorithm has strong ability to resist local nonlinear geometric attacks