{"title":"Single image camera identification using I-vectors","authors":"Arash Rashidi, F. Razzazi","doi":"10.1109/ICCKE.2017.8167913","DOIUrl":null,"url":null,"abstract":"Recently, in the field of speech processing, I-Vector modeling has been appealed a great deal of interest. I-Vector has shown its benefits in modeling of intra and inter-domain variabilities to a single low dimension space for speaker identification tasks. This paper presents the usage of I-Vector in camera identification as a new approach in image forensics domain. In our approach, image texture is extracted from images as our features for the I-vector system. We have used 8 camera models in our work and the result shows 99.01% accuracy. We have also conducted attacks on the test images. We gained 99.01% accuracy for rotation attack and the average accuracy of 88.71% for three level brightness attack.","PeriodicalId":151934,"journal":{"name":"2017 7th International Conference on Computer and Knowledge Engineering (ICCKE)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 7th International Conference on Computer and Knowledge Engineering (ICCKE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCKE.2017.8167913","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3
Abstract
Recently, in the field of speech processing, I-Vector modeling has been appealed a great deal of interest. I-Vector has shown its benefits in modeling of intra and inter-domain variabilities to a single low dimension space for speaker identification tasks. This paper presents the usage of I-Vector in camera identification as a new approach in image forensics domain. In our approach, image texture is extracted from images as our features for the I-vector system. We have used 8 camera models in our work and the result shows 99.01% accuracy. We have also conducted attacks on the test images. We gained 99.01% accuracy for rotation attack and the average accuracy of 88.71% for three level brightness attack.