F. Ahmed, F. Khelifi, Ashref Lawgaly, A. Bouridane
{"title":"“诺森比亚时间图像取证”数据库:描述和分析","authors":"F. Ahmed, F. Khelifi, Ashref Lawgaly, A. Bouridane","doi":"10.1109/CoDIT49905.2020.9263888","DOIUrl":null,"url":null,"abstract":"This paper introduces a standard digital picture dataset specifically designed for temporal digital image forensics. The database, called Northumbria Temporal Image Forensics (NTIF), consists of natural images with full high resolution of indoor and outdoor scenes. The images are organized in temporal order with regular acquisition timeslots spanned over for 94 weeks using ten digital camera devices. 41,684 images were captured from 10 digital cameras belonged to different models and brands. To this end, the subset of images has been annotated with labels spanning over categories based on the temporal factor of one to two weeks. Constructing such a large-scale temporal image database has been a challenging and enduring process. During the construction of NTIF, ethics were fully considered. The proposed dataset will be freely accessible to benefit all researchers in image forensics from academia and industry. This paper aims to describe the NTIF database and highlight the changes in Sensor Pattern Noise over time. Experiments have been conducted in which the correlations between noise residuals appear to be sensitive to the acquisition time of the respective digital images. The results show a clearly different pattern of correlations when the images are captured in different timeslots as compared to those images acquired within the same timeslots.","PeriodicalId":355781,"journal":{"name":"2020 7th International Conference on Control, Decision and Information Technologies (CoDIT)","volume":"92 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"The ‘Northumbria Temporal Image Forensics’ Database: Description and Analysis\",\"authors\":\"F. Ahmed, F. Khelifi, Ashref Lawgaly, A. Bouridane\",\"doi\":\"10.1109/CoDIT49905.2020.9263888\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper introduces a standard digital picture dataset specifically designed for temporal digital image forensics. The database, called Northumbria Temporal Image Forensics (NTIF), consists of natural images with full high resolution of indoor and outdoor scenes. The images are organized in temporal order with regular acquisition timeslots spanned over for 94 weeks using ten digital camera devices. 41,684 images were captured from 10 digital cameras belonged to different models and brands. To this end, the subset of images has been annotated with labels spanning over categories based on the temporal factor of one to two weeks. Constructing such a large-scale temporal image database has been a challenging and enduring process. During the construction of NTIF, ethics were fully considered. The proposed dataset will be freely accessible to benefit all researchers in image forensics from academia and industry. This paper aims to describe the NTIF database and highlight the changes in Sensor Pattern Noise over time. Experiments have been conducted in which the correlations between noise residuals appear to be sensitive to the acquisition time of the respective digital images. The results show a clearly different pattern of correlations when the images are captured in different timeslots as compared to those images acquired within the same timeslots.\",\"PeriodicalId\":355781,\"journal\":{\"name\":\"2020 7th International Conference on Control, Decision and Information Technologies (CoDIT)\",\"volume\":\"92 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-06-29\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2020 7th International Conference on Control, Decision and Information Technologies (CoDIT)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CoDIT49905.2020.9263888\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 7th International Conference on Control, Decision and Information Technologies (CoDIT)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CoDIT49905.2020.9263888","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
The ‘Northumbria Temporal Image Forensics’ Database: Description and Analysis
This paper introduces a standard digital picture dataset specifically designed for temporal digital image forensics. The database, called Northumbria Temporal Image Forensics (NTIF), consists of natural images with full high resolution of indoor and outdoor scenes. The images are organized in temporal order with regular acquisition timeslots spanned over for 94 weeks using ten digital camera devices. 41,684 images were captured from 10 digital cameras belonged to different models and brands. To this end, the subset of images has been annotated with labels spanning over categories based on the temporal factor of one to two weeks. Constructing such a large-scale temporal image database has been a challenging and enduring process. During the construction of NTIF, ethics were fully considered. The proposed dataset will be freely accessible to benefit all researchers in image forensics from academia and industry. This paper aims to describe the NTIF database and highlight the changes in Sensor Pattern Noise over time. Experiments have been conducted in which the correlations between noise residuals appear to be sensitive to the acquisition time of the respective digital images. The results show a clearly different pattern of correlations when the images are captured in different timeslots as compared to those images acquired within the same timeslots.