Camera/mobile phone source identification for digital forensics

被引:0
|
作者
Tsai, Min-Jen [1 ]
Lai, Cheng-Liang [2 ]
Liu, Jung [1 ]
机构
[1] Natl Chiao Tung Univ, Inst Informat Management, Hsinchu, Taiwan
[2] Fo Guang Univ, Inst Informat, Yilan, Taiwan
关键词
cameras; correlation; feature extraction;
D O I
暂无
中图分类号
O42 [声学];
学科分类号
070206 ; 082403 ;
摘要
Digital forensics has lately become one of the very important applications to identify the characteristics and the originality of the digital devices. This study has focused on analyzing the relationship between digital cameras and the photographs by using the support vector machine (SVM). Based on the fact that the internal imaging formation algorithms of the cameras are different from one manufacturer to another, our approach first calculates the characteristic values of the images taken by different cameras in conjunction with image processing techniques and data exploration methods. The training and categorization procedures of the image characteristic values are further conducted through SVM to identify the source camera of the images. Based on SVM's ability to distinguish cameras of different brands, this study also examines whether the method can differentiate cameras of the same brand, or even the popular mobile phones with camera. The experiment results demonstrate that our approach can achieve higher identification rate for camera and mobile phone sources than the results from other literatures.
引用
收藏
页码:221 / +
页数:2
相关论文
共 50 条
  • [21] Deep learning for source camera identification on mobile devices
    Freire-Obregon, David
    Narducci, Fabio
    Barra, Silvio
    Castrillon-Santana, Modesto
    PATTERN RECOGNITION LETTERS, 2019, 126 : 86 - 91
  • [22] SCCRNet: a framework for source camera identification on digital images
    Sychandran, C. S.
    Shreelekshmi, R.
    NEURAL COMPUTING & APPLICATIONS, 2024, 36 (03): : 1167 - 1179
  • [23] SCCRNet: a framework for source camera identification on digital images
    C. S. Sychandran
    R. Shreelekshmi
    Neural Computing and Applications, 2024, 36 : 1167 - 1179
  • [24] Mobile phone forensics: an overview of technical and legal aspects
    Induruwa, Abhaya
    INTERNATIONAL JOURNAL OF ELECTRONIC SECURITY AND DIGITAL FORENSICS, 2009, 2 (02) : 169 - 181
  • [25] Adaptive feature selection for digital camera source identification
    Tsai, Min-Jen
    Wang, Cheng-Sheng
    PROCEEDINGS OF 2008 IEEE INTERNATIONAL SYMPOSIUM ON CIRCUITS AND SYSTEMS, VOLS 1-10, 2008, : 412 - 415
  • [26] Simulator of Camera Accelerometer for Mobile Phone
    Yin QiLong
    Li Hui
    Huang XiaoDong
    PROCEEDINGS OF INTERNATIONAL SYMPOSIUM ON IMAGE ANALYSIS & SIGNAL PROCESSING, 2009, 2009, : 63 - 65
  • [27] Instagram Mobile Application Digital Forensics
    Mubarik, Muhammad Asim
    Wang, Zhijian
    Nam, Yunyoung
    Kadry, Seifedine
    Waqar, Muhammad Azam
    COMPUTER SYSTEMS SCIENCE AND ENGINEERING, 2021, 37 (02): : 169 - 186
  • [28] Analysis of Mobile Phones in Digital Forensics
    Dogan, Sengul
    Akbal, Erhan
    2017 40TH INTERNATIONAL CONVENTION ON INFORMATION AND COMMUNICATION TECHNOLOGY, ELECTRONICS AND MICROELECTRONICS (MIPRO), 2017, : 1241 - 1244
  • [29] DIGITAL IMAGE PROCESSING IN LED VISIBLE LIGHT COMMUNICATIONS USING MOBILE PHONE CAMERA
    Li, Zongze
    Zhang, Zhenshan
    Yuan, Qiaozhi
    Qiao, Yaojun
    Liao, Ke
    Yu, Haihua
    PROCEEDINGS OF 2016 5TH IEEE INTERNATIONAL CONFERENCE ON NETWORK INFRASTRUCTURE AND DIGITAL CONTENT (IEEE IC-NIDC 2016), 2016, : 239 - 243
  • [30] Low-Cost Mobile Phone Microscopy with a Reversed Mobile Phone Camera Lens
    Switz, Neil A.
    D'Ambrosio, Michael V.
    Fletcher, Daniel A.
    PLOS ONE, 2014, 9 (05):