Filters Ranking for DWT-Domain Robust Digital Watermarking
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Filters Ranking for DWT-Domain Robust Digital Watermarking Martin Dietze Department of Information Systems, University of Buckingham, Buckingham MK18 1EG, UK Email: [email protected]
Sabah Jassim Department of Information Systems, University of Buckingham, Buckingham MK18 1EG, UK Email: [email protected] Received 30 March 2003; Revised 24 September 2003 In recent years a number of wavelet-based watermarking schemes have been proposed and exhibited improved qualities. The choice of a wavelet filter bank for a digital watermarking scheme can have a significant influence on the scheme’s performance in terms of image quality and robustness. We present the results of experiments conducted using two different embedding algorithms (one blind and one nonblind) using a number of popular filter banks. The aim is to find filters that exhibit optimal performance with respect to specified requirements. The results demonstrate that the subband depth of embedding has the most significant influence on the filter bank choice. The kind of attack and the kind of embedding are also important, while marking intensity and compression ratio seem to affect the performance to a less extent. Additionally we show that out of the two embedding methods the quantization-based blind one leads to better overall results than the popular, nonblind one. Keywords and phrases: watermarking, embedding, SCS, spread spectrum, wavelet, filters.
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INTRODUCTION
Robust digital watermarking (e.g., for copyright protection) has gained increasing importance with the availability and popularity of Internet and eCommerce applications. Digital object formats do not restrict copying or further distribution of image files. Watermarking is used to assert rightful ownership or track down pirate copies by previous invisible embedding of a logo or a serial number into the file. The performance of watermarking schemes is measured in terms of two rather contradicting requirements: imperceptibility (i.e., optimally minimum image degradation) and robustness (i.e., withstanding various attacks that aim to remove the watermark or render it undetectable). Benchmarking tools like StirMark [1] combine most attacks and show that most existing watermarking schemes are vulnerable. The advantages of DWT-based watermarking are wellaccepted, still apart from our own work [2] little is said in the literature about how the choice of a filter bank affects a watermarking scheme’s performance. In [3], Fei et al. discuss the choice of a transform domain for watermarking, and in [4], Wolfgang et al. look at the effect of matching the domain of marking to the domain for lossy compression, yet both papers do not discuss the effect of a chosen domain’s individual parameters.
Besides the choice of a filter bank, a DWT marking scheme’s performance depends on features, like subband depth and the decomposition scheme used. Characteristics shared with nonwavelet-based schemes are the embedding technique and embedding intensity. Due to the DWT’s spatial support, variation in t
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