Forensic Source Camera Classification

Beneficiary:      Luca Debiasi, University of Salzburg, Salzburg (AT)

Host:                 Kristin Norell, Swedish National Forensic Centre, Linköping (SE)

Period:              14/10/2015 to 13/11/2015

 

1.    Purpose of the STSM

The main purpose of this STSM was to investigate a large data set containing images from an unknown number of different cameras and cluster it according to the source camera which acquired the images by exploiting the photo-response non-uniformity (PRNU). The data set under investigation contains images from unknown source cameras found on a personal computer as part of an actual criminal case. If the method is suitable it could be used in the near future at the Swedish National Forensic Centre (NFC) for example to cluster images from the same camera in child molest cases and in that way identify more victims. Connecting images to each other through the camera used could also help to identify the perpetrator.

 

2.    Work carried out during the STSM

  • Implementation of different PRNU enhancement techniques.
  • Analysis of EXIF data for images in case data set.
  • Implementation of various clustering techniques from literature and development of novel one.
  • Evaluation of clustering performance on “Dresden Image Database”
  • Extracted the PRNU of case data images using various sizes and positions.
  • Clustering of case data using the different clustering techniques.
  • Evaluation of the clustering results for the case data experiments and discussion with NFC experts on future application.
  • Presentation of “A Framework for Decision Fusion in Image Forensics Based on Dempster–Shafer Theory of Evidence” paper for decision fusion and discussion.
  • PRNU related discussions with experts from NFC: camera identification and existing protocol applied at NFC, PRNU quality and enhancements, inter-camera similarities and resulting false positives, decision fusion application for forensic investigations.

 

3.    Outcome and future collaboration

The analysis of the EXIF data showed 60 different camera models from 14 different makes for the images where the EXIF data was available. The implemented PRNU enhancements have been tested on a subset of the “Dresden Image Database” by performing camera identification experiments, where it was possible to lower the error rates and improve the differentiability of the cameras. The results of the application of the clustering techniques to the case data have been discussed and several random samples have been verified manually regarding their plausibility by looking at the EXIF data of the images and identify misclassifications. Some of the techniques have been able to generate a plausible and realistic number of clusters, while other methods showed to be not suited for this kind of data. Since most methods give a higher number of clusters than estimated from the EXIF data a subsequent merging of clusters after the clustering is also considered for future work. Since not all of the planned work could be completed during the STSM, the implementation of decision fusion strategies and camera identification for HDR images are performed following to the STSM. Therefore, the collaboration is still ongoing.