Audio Watermarking, Steganalysis Using Audio Quality Metrics, and Robust Audio Hashing

We propose a technique for the problem of detecting the very presence of hidden messages in an audio object. The detector is based on the characteristics of the denoised residuals of the audio file. Our proposition is established upon the presupposition that the hidden message in a cover object leaves statistical evidence that can be detected with the use of some audio distortion measures. The distortions caused by hidden message are measured in terms of objective and perceptual quality metrics. The detector discriminates between cover and stego files using a selected subset of features and an SVM classifier. We have evaluated the detection performance of the proposed steganalysis technique with the well-known watermarking and steganographic methods. We present novel and robust audio fingerprinting techniques based on the summarization of the time-frequency spectral characteristics of an audio object. The perceptual hash functions are based on the periodicity series of the fundamental and on the singular-value description of the cepstral frequencies. The proposed hash functions are found, on the one hand, to perform very satisfactorily in identification and verification tests, and on the other hand, to be very resilient to a large variety of attacks. Moreover we address the issue of security of hashes and propose a keying technique, thus a key dependent hashing. We also present a non-oblivious, extremely robust watermarking scheme for audio signals. The watermarking algorithm is based on the SVD of the spectrogram of the signal. Thus the SVD of the spectrogram is modified according to the watermarking bits. The algorithm is tested for inaudibility performance with audio quality measures and robustness tests with audio stirmark benchmark tool, which have a variety of common signal processing distortions. The mean bit error rate is 0.629 percent.