Automatic source camera identification using the intrinsic lens radial distortion.
TLDR
It is demonstrated that it is possible to achieve a high rate of accuracy in the identification of source camera identification by noting the intrinsic lens radial distortion of each camera.Abstract:
Source camera identification refers to the task of matching digital images with the cameras that are responsible for producing these images. This is an important task in image forensics, which in turn is a critical procedure in law enforcement. Unfortunately, few digital cameras are equipped with the capability of producing watermarks for this purpose. In this paper, we demonstrate that it is possible to achieve a high rate of accuracy in the identification by noting the intrinsic lens radial distortion of each camera. To reduce manufacturing cost, the majority of digital cameras are equipped with lenses having rather spherical surfaces, whose inherent radial distortions serve as unique fingerprints in the images. We extract, for each image, parameters from aberration measurements, which are then used to train and test a support vector machine classifier. We conduct extensive experiments to evaluate the success rate of a source camera identification with five cameras. The results show that this is a viable approach with high accuracy. Additionally, we also present results on how the error rates may change with images captured using various optical zoom levels, as zooming is commonly available in digital cameras.read more
Citations
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Journal ArticleDOI
An Overview on Image Forensics
TL;DR: The aim of this survey is to provide a comprehensive overview of the state of the art in the area of image forensics by classifying the tools according to the position in the history of the digital image in which the relative footprint is left: acquisition- based methods, coding-based methods, and editing-based schemes.
Journal ArticleDOI
Vision of the unseen: Current trends and challenges in digital image and video forensics
TL;DR: The emerging field of digital image forensics is introduced, including the main topic areas of source camera identification, forgery detection, and steganalysis, including a critical analysis of the state of the art, and recommendations for the direction of future research.
Proceedings ArticleDOI
Can we trust digital image forensics
TL;DR: This work will take a closer look at two state-of-the-art forensic methods and proposes two counter-techniques; one to perform resampling operations undetectably and another one to forge traces of image origin.
Journal ArticleDOI
First Steps Toward Camera Model Identification with Convolutional Neural Networks
TL;DR: Zhang et al. as discussed by the authors proposed a data-driven algorithm based on convolutional neural networks, which learns features characterizing each camera model directly from the acquired pictures, and showed that the proposed method outperforms up-to-date state-of-the-art algorithms on classification of 64 × 64 color image patches.
Journal ArticleDOI
Blind Identification of Source Cell-Phone Model
TL;DR: This paper demonstrates that the camera model identification algorithm achieves more accurate identification, and that it can be made robust to a host of image manipulations.
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A versatile camera calibration technique for high-accuracy 3D machine vision metrology using off-the-shelf TV cameras and lenses
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