Variant length, Self-extracted audio watermark for verification using LWT and random selections


In the last decade with the expansion of cyber multimedia activities, concepts like authentication, identification and verification became a must. Audio is one of the challenging media in cyber security for its complicated nature. Watermarking rises as an important methods used in securing audio files and other media. In this research a new method is used for extracting the signal features from random positions in the original audio signals by some signal calculations in time domain and hide them within the same audio in other positions after transforming the samples in these positions using lift wavelet transform, all positions were chosen depending on random walk method and a secret key. The extracted features will be compared with the hidden features (watermark) for verification. The proposed method was tested against compression (mp3) and noise addition (White Gaussian noise). Many types of performance measurements like peak signal to noise ratio, bit error rat, mean square error and others were used to measure the efficiency of the proposed method.