Evaluation of IQM's effectiveness for cell phone identification using captured videos and images

Images and videos captured by cell phones are very important carriers of information amongst user generated multimedia contents. These carriers can be used in solving many forensic problems such as identification of movie piracy, insurance cases, child pornography, and other applications involving identifying/verifying source cell phones. This paper evaluates the effectiveness of Image Quality Measures (IQM) for identifying the source cell phone from the images or videos captured by that cell phone, by comparing the classification accuracies obtained for these two scenarios. Twenty-eight IQM features for each image and selected video frame are extracted and then classified using the Rotation forest classifier of WEKA. The proposed method is tested on 900 images and 1,350 short videos from nine different cellphones, some of which are of same brands and models. The experiments demonstrate that due to larger compression artifacts in videos, IQM are less effective for video based source cell phone identification as compared to image based source cell phone identification.

[1]  Ian Witten,et al.  Data Mining , 2000 .

[2]  Paolo Bestagini,et al.  An overview on video forensics , 2012, 2012 Proceedings of the 20th European Signal Processing Conference (EUSIPCO).

[3]  Judith Redi,et al.  Digital image forensics: a booklet for beginners , 2010, Multimedia Tools and Applications.

[4]  Nasir D. Memon,et al.  Steganalysis using image quality metrics , 2003, IEEE Trans. Image Process..

[5]  Ian H. Witten,et al.  Data mining: practical machine learning tools and techniques with Java implementations , 2002, SGMD.

[6]  Ian H. Witten,et al.  Data mining: practical machine learning tools and techniques, 3rd Edition , 1999 .

[7]  Nasir D. Memon,et al.  Blind source camera identification , 2004, 2004 International Conference on Image Processing, 2004. ICIP '04..

[8]  Bertrand Chupeau,et al.  A framework for video forensics based on local and temporal fingerprints , 2009, 2009 16th IEEE International Conference on Image Processing (ICIP).

[9]  Bülent Sankur,et al.  Blind Identification of Source Cell-Phone Model , 2008, IEEE Transactions on Information Forensics and Security.

[10]  Ats Ho,et al.  Inter-Camera Model Image Source Identification with Conditional Probability Features , 2012 .

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

[12]  Takahiro Okabe,et al.  Detecting Forgery From Static-Scene Video Based on Inconsistency in Noise Level Functions , 2010, IEEE Transactions on Information Forensics and Security.