Title
Logarithmic Spread-Transform Dither Modulation watermarking Based on Perceptual Model
Abstract
Logarithmic Quantization Index Modulation (LQIM) is an important extension of the original quantization-based watermarking method. However, it is well known that it is sensitive to valumetric scaling attack and easy to result in sign error after quantization and attacks. For that, in this paper, we propose a new method, namely Logarithmic Spread-Transform Dither Modulation Based on Perceptual Model (LSTDM-WM). In this regard the host signal is first projected onto a random vector and transformed using a novel Logarithmic Quantization function. Then the transformed signal is quantized regarding the watermark data and the watermarked signal is obtained by applying inverse transform to the quantized signal. The perceptual model is further exploited to adjust the quantization step adaptively for watermark embedding. Experimental results indicate that our proposed scheme overcomes two challenges cited above and has superior performance in comparison with conventional LQIM and former proposed schemes of STDM.
Year
DOI
Venue
2013
10.1109/ICIP.2013.6738931
ICIP
Keywords
Field
DocType
digital watermarking,watermark data,random processes,watermark embedding,random vector,quantisation (signal),quantization-based watermarking method,lqim,lstdm-wm,watermarking,inverse transforms,lstdm-mw,logarithmic spread-transform dither modulation based on perceptual model,watson's perceptual model,logarithmic quantization index modulation,watermarked signal,volumetric scaling attack,logarithmic spread-transform dither modulation watermarking,quantized signal,inverse transform,vectors,logarithmic quantization function,stdm
Digital watermarking,Pattern recognition,Computer science,Stochastic process,Modulation,Watermark,Multivariate random variable,Quantization (physics),Artificial intelligence,Logarithm,Quantization (signal processing)
Conference
ISSN
Citations 
PageRank 
1522-4880
1
0.36
References 
Authors
3
6
Name
Order
Citations
PageRank
Wenbo Wan16313.02
Ju Liu239652.06
Jiande Sun323241.76
Xiaohui Yang4493.57
Xiushan Nie521835.22
Feng Wang610.70