Title : 
Two-band amplitude scale estimation for quantization-based watermarking
         
        
            Author : 
Wang, Jinshen ; Shterev, Ivo D. ; Lagendijk, Reginald L.
         
        
            Author_Institution : 
Delft Univ. of Technol., Netherlands
         
        
        
        
        
        
            Abstract : 
Quantization-based watermarking schemes comprise a class of watermarking schemes that achieves channel capacity in terms of additive noise attacks. Unfortunately, quantization-based watermarking schemes are not robust against linear time invariant (LTI) filtering attacks. In this paper we concentrate on amplitude scaling of a multi-band system. First we derive the probability density function (PDF) of the attacked data. Second, using a simplified approximation of the PDF model, we derive a maximum likelihood (ML) procedure for estimating two-band amplitude scaling factor. Finally, experiments are performed with real audio signals showing the good performance of the proposed estimation technique under realistic conditions.
         
        
            Keywords : 
amplitude estimation; audio coding; filtering theory; maximum likelihood estimation; quantisation (signal); watermarking; additive noise attacks; audio signals; channel capacity; filtering attacks; linear time invariant; maximum likelihood; multi-band system; probability density function; quantization-based watermarking; two-band amplitude scale estimation; Additive noise; Amplitude estimation; Decoding; Filter bank; Filtering; Frequency conversion; Maximum likelihood estimation; Nonlinear filters; Quantization; Watermarking; maximum likelihood estimation; multi-band; quantization; watermarking;
         
        
        
        
            Conference_Titel : 
Intelligent Signal Processing and Communication Systems, 2005. ISPACS 2005. Proceedings of 2005 International Symposium on
         
        
            Print_ISBN : 
0-7803-9266-3
         
        
        
            DOI : 
10.1109/ISPACS.2005.1595365