Rajat Sharma, Nishtha Agarwal, Krittika Khanwalkar, Manasvee Singh, Dharmendra Kumar
{"title":"Robust Image Watermarking Technique Using Contourlet Transform and Optimized Edge Detection Algorithm","authors":"Rajat Sharma, Nishtha Agarwal, Krittika Khanwalkar, Manasvee Singh, Dharmendra Kumar","doi":"10.1109/SPIN.2018.8474052","DOIUrl":null,"url":null,"abstract":"In the direction of attaining greater robustness against various signal processing operations, this paper proposes a new image watermarking scheme in multiple transform domain. Firstly, the host image is decomposed using Discrete Wavelet Transform (DWT) and the lower frequency subband is obtained. This subband is further decomposed using two-level Coutourlet Transform(CT) wherein the first level approximate subband is used to evaluate the optimized modification parameters and the second level detail subband is used for watermark embedding. In counterpart of embedding, the watermark bits are extracted based on the guiding factor. Experimental results show higher robustness against various attacks without compromising the image quality.","PeriodicalId":184596,"journal":{"name":"2018 5th International Conference on Signal Processing and Integrated Networks (SPIN)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 5th International Conference on Signal Processing and Integrated Networks (SPIN)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SPIN.2018.8474052","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2
Abstract
In the direction of attaining greater robustness against various signal processing operations, this paper proposes a new image watermarking scheme in multiple transform domain. Firstly, the host image is decomposed using Discrete Wavelet Transform (DWT) and the lower frequency subband is obtained. This subband is further decomposed using two-level Coutourlet Transform(CT) wherein the first level approximate subband is used to evaluate the optimized modification parameters and the second level detail subband is used for watermark embedding. In counterpart of embedding, the watermark bits are extracted based on the guiding factor. Experimental results show higher robustness against various attacks without compromising the image quality.