Source identification for mobile devices, based on wavelet transforms combined with sensor imperfections

One of the most relevant applications of digital image forensics is to accurately identify the device used for taking a given set of images, a problem called source identification. This paper studies recent developments in the field and proposes the mixture of two techniques (Sensor Imperfections and Wavelet Transforms) to get better source identification of images generated with mobile devices. Our results show that Sensor Imperfections and Wavelet Transforms can jointly serve as good forensic features to help trace the source camera of images produced by mobile phones. Furthermore, the model proposed here can also determine with high precision both the brand and model of the device.

[1]  Minh N. Do,et al.  Ieee Transactions on Image Processing the Contourlet Transform: an Efficient Directional Multiresolution Image Representation , 2022 .

[2]  Fanjie Meng,et al.  Source Camera Identification Forensics Based on Wavelet Features , 2009, 2009 Fifth International Conference on Intelligent Information Hiding and Multimedia Signal Processing.

[3]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[4]  Jiebo Luo,et al.  Photo classification by integrating image content and camera metadata , 2004, Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004..

[5]  Trygve Randen,et al.  Filtering for Texture Classification: A Comparative Study , 1999, IEEE Trans. Pattern Anal. Mach. Intell..

[6]  Richard L. Baer.  Resolution Limits in Digital Photography: The Looming End of the Pixel Wars , 2010 .

[7]  B. Sankur,et al.  Source Cell-phone Identification , 2006, 2006 IEEE 14th Signal Processing and Communications Applications.

[8]  Mohan S. Kankanhalli,et al.  A Survey on Digital Camera Image Forensic Methods , 2007, 2007 IEEE International Conference on Multimedia and Expo.

[9]  Jelena Tesic.  Metadata Practices for Consumer Photos , 2005, IEEE Multim..

[10]  Jan P. Allebach,et al.  Forensic classification of imaging sensor types , 2007, Electronic Imaging.

[11]  Nasir D. Memon,et al.  Classification of digital camera-models based on demosaicing artifacts , 2008, Digit. Investig..

[12]  Zeno Geradts,et al.  Methods for identification of images acquired with digital cameras , 2001, SPIE Optics East.

[13]  Josef Kittler,et al.  Floating search methods in feature selection , 1994, Pattern Recognit. Lett..

[14]  Miroslav Goljan,et al.  Digital camera identification from sensor pattern noise , 2006, IEEE Transactions on Information Forensics and Security.

[15]  Jiebo Luo,et al.  Beyond pixels: Exploiting camera metadata for photo classification , 2005, Pattern Recognit..

[16]  Min Wu,et al.  Image acquisition forensics: Forensic analysis to identify imaging source , 2008, 2008 IEEE International Conference on Acoustics, Speech and Signal Processing.

[17]  Ismail Avcibas,et al.  Differentiating Between Images Using Wavelet-Based Transforms: A Comparative Study , 2011, IEEE Transactions on Information Forensics and Security.

[18]  Jorge Serrano-Cobos,et al.  Recovery of descriptive information in images from digital libraries by means of EXIF metadata , 2008, Libr. Hi Tech.

[19]  Min-Jen Tsai,et al.  Camera/Mobile Phone Source Identification for Digital Forensics , 2007, 2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP '07.