Music content authentication based on beat segmentation and fuzzy classification

Abstract

Digital audio has been ubiquitous over the past decade. Since it can be easily modified by editing tools, there has been a strong need to protect its content for secure multimedia applications. Previous audio authentication algorithms are mainly focused on either human speech or general audio with music as part of the test data, while special research on music authentication has been somewhat neglected. In this article, we propose a novel algorithm to protect the integrity and authenticity of music signals. Its main contributions include the following: (1) Music is segmented into beat-based frames, which not only endows the authentication units with more semantic meaning but also perfectly resolves the challenging synchronization problem. (2) Robust hashes are generated from chroma-based mid-level audio feature which can appropriately characterize the music content and integrated with an encryption procedure to ensure the security against malicious block-wise vector quantization attack. (3) Fuzzy logic is adopted to make the authentication decision in the light of three measures defined on bit errors, coinciding with the inherent blurred nature of authentication. The experiments exhibit good discriminative ability between admissible and malicious operations.