Analysis of a novel audio hash function based upon stationary wavelet transform

Robust hashing for multimedia authentication is an emerging research area. Audio hash functions provide a tool for fast and reliable identification of content. A different key-dependent robust audio hashing based upon speech construction model is proposed in this article. The proposed audio hash function is based on the essential frequency series. Robust hash is calculated based on linear spectrum frequencies (LSFs) which model the verbal territory. The correlation between LSFs is decoupled by Stationary wavelet transform (SWT). A randomization structure controlled by a secret key is used in hash generation for random feature selection. The audio hash function is key-dependent and collision resistant. Temporarily, it is extremely robust to content protective operations besides having high accuracy of tampering localization. They are found, the first, to perform very adequately in identification and verification tests, and the second, to be very robust to a large range of attacks. Furthermore, it can be addressed the issue of security of hashes and proposed a keying technique, and thereby a key-dependent audio hash function.