Video source camera identification using fusion of texture features and noise fingerprint

Abstract

In Video forensics, the objective of Source Camera Identification (SCI) is to identify and verify the origin of a video that is under investigation. This aids the investigator to trace the video to its owner or narrow down the search space for identifying the offender. Nowadays, it is easy to record and share videos via internet or social media with smartphones. The availability of sophisticated video editing tools and software allow offenders to modify video's context. Thus, identifying the right source camera that was used to capture the video becomes complicated and strenuous. Existing methods based on video metadata information are no longer reliable as it could be modified or stripped off. Better forensic procedures are therefore required to prove the authenticity and integrity of the video that will be used as evidence in court of law. Certain inherent camera sensor properties such as, subtle traces of Photo Response Non-Uniformity (PRNU) are present in all captured videos due to unnoticeable defect during the manufacture of camera's sensor. These properties are used in SCI to classify devices or models as they are unique. In this work, we focus on SCI from videos or Video Source Camera Identification (VSCI) to verify the authenticity of videos. PRNU can be affected by highly textured content or post-processing when computed from a set of flat field images. To mitigate these effects, Higher Order Wavelet Statistics (HOWS) information from PRNU of a video I-frame is combined with information from two other texture features i.e., Local Binary Pattern (LBP) and Gray Level Co-occurrence Matrix (GLCM). The extracted feature vector is fused via concatenation and fed to Support Vector Machine (SVM) classifier to perform training and testing for VSCI. Experimental evaluation of our proposed method on videos from different publicly available datasets show the effectiveness of our method in terms of accuracy, resource efficiency, and complexity.