Performance Evaluation of Source Camera Attribution by Using Likelihood Ratio Methods

Abstract

Performance evaluation of source camera attribution methods typically stop at the level of analysis of hard to interpret similarity scores. Standard analytic tools include Detection Error Trade-off or Receiver Operating Characteristic curves, or other scalar performance metrics, such as Equal Error Rate or error rates at a specific decision threshold. However, the main drawback of similarity scores is their lack of probabilistic interpretation and thereby their lack of usability in forensic investigation, when assisting the trier of fact to make more sound and more informed decisions. The main objective of this work is to demonstrate a transition from the similarity scores to likelihood ratios in the scope of digital evidence evaluation, which not only have probabilistic meaning, but can be immediately incorporated into the forensic casework and combined with the rest of the case-related forensic. Likelihood ratios are calculated from the Photo Response Non-Uniformity source attribution similarity scores. The experiments conducted aim to compare different strategies applied to both digital images and videos, by considering their respective peculiarities. The results are presented in a format compatible with the guideline for validation of forensic likelihood ratio methods.

PRNU-Based Video Source Attribution: Which Frames Are You Using?

“…a strategy that could be optimal both in presence of EIS and not is difficult to be achieved. In fact, the literature [ 21 , 31 ] shows that for non-stabilized videos, the optimal solution is to aggregate PRNUs extracted from all frames, or at least all the I frames, according to Equation ( 1 ). At the same time, this approach can hardly be applied to the case of stabilized videos.…”

Image and Video Forensics

“…De Roos and Geradts [9] investigated different factors, such as resolution, length of the video and compression, that influence camera video identification based on PRNU (photo response non-uniformity noise). To this end, Ferrara et al [10] presented a new approach for the performance evaluation of source camera attribution by using likelihood ratio methods obtained from the PRNU similarity scores. Dal Cortivo et al [11] investigated the camera model identification on video proposing a CNN (Convolutional Neural Network) based method jointly exploit audio and visual information.…”