Conference proceeding
Detecting anti-forensic attacks on demosaicing-based camera model identification
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) Conference Proceedings, 1512
01 Jan 2017
Abstract
Conference Title: 2017 IEEE International Conference on Image Processing (ICIP) Conference Start Date: 2017, Sept. 17 Conference End Date: 2017, Sept. 20 Conference Location: Beijing, China Many forensic algorithms have been developed to determine the model of an image's source camera by examining traces left by the camera's demosaicing algorithm. An anti-forensic attacker, however, can falsify these traces by maliciously using existing forensic techniques to estimate one camera's demosaicing filter, then use these estimates to re-demosaic an image captured by a different camera. Currently, there is no known defense against this attack, which is capable of fooling existing camera model identification algorithms. In this paper, we propose a new method to detect if an image's source camera model has been anti-forensically falsified. Our algorithm operates by characterizing the different content-independent local pixel relationships that are introduced by both authentic demosaicing algorithms and anti-forensic attacks. Experimental results show that our algorithm can detect an anti-forensic attack with over 99% accuracy, is robust to JPEG compression, and can even identify the true source camera model in certain circumstances.
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Details
- Title
- Detecting anti-forensic attacks on demosaicing-based camera model identification
- Creators
- Chen ChenXinwei ZhaoMatthew C Stamm
- Publication Details
- The Institute of Electrical and Electronics Engineers, Inc. (IEEE) Conference Proceedings, 1512
- Publisher
- The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Electrical and Computer Engineering
- Identifiers
- 991019170506504721