Abstract
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.
Highlights
Robust digital watermarking has gained increasing importance with the availability and popularity of Internet and eCommerce applications
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
Summary
Robust digital watermarking (e.g., for copyright protection) has gained increasing importance with the availability and popularity of Internet and eCommerce applications. Besides the choice of a filter bank, a DWT marking scheme’s performance depends on features, like subband depth and the decomposition scheme used. Recent watermarking schemes use a variety of different measures to achieve robustness. Most such schemes (see [5]) have a number of things in common: significant wavelet coefficients are chosen for embedding, information is embedded in single coefficients (normally through additive/multiplicative embedding), and often both blind or nonblind embedding are possible, resulting in different levels of robustness. We use 7 quality levels of JPEG compression and, to test for dependencies between the results and the kind of compression, a DWT-based compression at 4 different ratios as an attack on 8 different images.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
