Digital camera identification using PRNU: A feature based approach |
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| Affiliation: | 1. Department of Electronics and Communication Engineering, NMAMIT Nitte, Karkala, 574110, India;2. Department of Computer Applications, NMAMIT Nitte, Karkala, 574110, India;3. Department of Instrumentation and Control Engineering, NMAMIT Nitte, Karkala, 574110, India;4. Department of Electrical and Electronics Engineering, NMAMIT Nitte, Karkala, 574110, India;5. Manipal Institute of Technology, Manipal University, Manipal, 576104, Karnataka, India;1. Institute of Research and Development, Duy Tan University, 182 Nguyen Van Linh, Da Nang, Viet Nam;2. ICD, LM2S, ROSAS, UMR 6281, CNRS, Troyes University of Technology, 12 rue Marie Curie, 10010 Troyes cedex, France;1. Department of Computer Science and Technology, Qilu University of Technology (Shandong Academy of Sciences), 3501 DaXue Road, JiNan, SD, PR China;2. Shenzhen Key Laboratory of Media Information Content Security, PR China;1. School of Information and Communication Engineering, Dalian University of Technology, China;2. College of Computer Science and Software Engineering, Shenzhen University, China;1. Media Integration and Communication Center (MICC), Università degli Studi di Firenze, Viale Morgagni 65, 50134 Firenze, Italy;2. National Interuniversity Consortium for Telecommunications - CNIT, Parma, Italy;3. Polo Tecnologico, Presidenza del Consiglio dei Ministri, Rome, Italy |
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| Abstract: | Source camera identification is one of the emerging field in digital image forensics, which aims at identifying the source camera used for capturing the given image. The technique uses photo response non-uniformity (PRNU) noise as a camera fingerprint, as it is found to be one of the unique characteristic which is capable of distinguishing the images even if they are captured from similar cameras. Most of the existing PRNU based approaches are very sensitive to the random noise components existing in the estimated PRNU, and also they are not robust when some simple manipulations are performed on the images. Hence a new feature based approach of PRNU is proposed for the source camera identification by choosing the features which are robust for image manipulations. The PRNU noise is extracted from the images using wavelet based denoising method and is represented by higher order wavelet statistics (HOWS), which are invariant features for image manipulations and geometric variations. The features are fed to support vector machine classifiers to identify the originating source camera for the given image and the results have been verified by performing ten-fold cross validation technique. The experiments have been carried out using the images captured from various cell phone cameras and it demonstrated that the proposed algorithm is capable of identifying the source camera of the given image with good accuracy. The developed technique can be used for differentiating the images, even if they are captured from similar cameras, which belongs to same make and model. The analysis have also showed that the proposed technique remains robust even if the images are subjected to simple manipulations or geometric variations. |
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| Keywords: | Source camera identification (SCI) Photo response non-uniformity (PRNU) Higher order wavelet statistics (HOWS) Support vector machines (SVM) Cross validation |
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