| Paper: | MLSP-P3.8 | ||
| Session: | Pattern Recognition | ||
| Time: | Wednesday, May 17, 14:00 - 16:00 | ||
| Presentation: | Poster | ||
| Topic: | Machine Learning for Signal Processing: Signal detection, Pattern Recognition and Classification | ||
| Title: | ROBUST AUDIO WATERMARK DECODING BY SUPERVISED LEARNING | ||
| Authors: | Serap Kirbiz, Bige Gunsel, Istanbul Technical University, Turkey | ||
| Abstract: | Most of the watermark (WM) decoding schemes use correlation-based methods because of their simplicity. In these methods, the WM signal embedded through a secret key is assumed as uncorre-lated with the host signal. This is a hard restriction that can never be achieved and correlation between the received signal and the secret key becomes greater than zero even though the received signal is un-watermarked. Mostly a decision threshold specified semi-automatically is used at the decoding site. Since the audio water-marking is a nonlinear process that guarantees the inaudibility, there is no analytic way of determining an optimal threshold value that makes the WM decoding problem harder. This paper introduces a learning scheme followed by a nonlinear classification thus elimi-nates the threshold specification problem. The decoding process is modelled as a three-class classification problem and Support Vector Machines (SVMs) are used in the learning of the embedded data. The decoding and detection performances of the developed system are greater than 98% and 95%, respectively. When the Watermark-to-Signal-Ratio (WSR) is higher than -30dB, system false alarm ratios remain less than 2%. It is shown that the introduced WM decoding method is robust to additive noise and most of add/remove and filter attacks of Stirmark. | ||
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