Psycho-acoustic model-based message authentication coding for audio data

Existing security algorithms for data authentication like cryptographic hash functions for example, are very sensitive to changes in the protected data by design. If these mechanisms are applied on multimedia data, they fail to distinguish imperceptible signal transformation from malicious tampering that perceivably changes the content. Furthermore, they usually provide no localization or an assessment of the relevance of such manipulations with respect to human perception or even semantics which is not desirable in a number of applications. We present an algorithm for a robust message authentication code (MAC) for digital audio data in the context of authentication watermarking. An existing audio fingerprinting approaches is improved with respect to key-based security. Psychoacoustic modelling is incorporated by us to separate inaudible changes from perceivably relevant modifications of the audio content. Experimental results show that the proposed algorithm provides both a high level of distinction between perceptually different audio data and a high robustness against signal transformations that do not change the perceived information.