Mitigation of H.264 and H.265 Video Compression for Reliable PRNU Estimation

The photo-response non-uniformity (PRNU) is a distinctive image sensor characteristic, and an imaging device inadvertently introduces its sensor's PRNU into all media it captures. Therefore, the PRNU can be regarded as a camera fingerprint and used for source attribution. The imaging pipeline in a camera, however, involves various processing steps that are detrimental to PRNU estimation. In the context of photographic images, these challenges are successfully addressed and the method for estimating a sensor's PRNU pattern is well established. However, various additional challenges related to generation of videos remain largely untackled. With this perspective, this work introduces methods to mitigate disruptive effects of widely deployed H.264 and H.265 video compression standards on PRNU estimation. Our approach involves an intervention in the decoding process to eliminate a filtering procedure applied at the decoder to reduce blockiness. It also utilizes decoding parameters to develop a weighting scheme and adjust the contribution of video frames at the macroblock level to PRNU estimation process. Results obtained on videos captured by 28 cameras show that our approach increases the PRNU matching metric up to more than five times over the conventional estimation method tailored for photos

Extracting PRNu noise from H.264 coded videos

26th European Signal Processing Conference (2018 : Rome; Italy)Every device equipped with a digital camera has a unique identity. This phenomenon is essentially due to a systematic noise component of an imaging sensor, known as photo-response non-uniformity (PRNU) noise. An imaging sensor inadvertently introduces this noise pattern to all media captured by that imaging sensor. The procedure for extracting PRNU noise has been well studied in the context of photographic images, however, its extension to video has so far been neglected. In this work, considering H.264 coding standard, we describe a procedure to extract sensor fingerprint from non-stabilized videos. The crux of our method is to remove a filtering procedure applied at the decoder to reduce blockiness and to use macroblocks selectively when estimating PRNU noise pattern. Results show that our method has a potential to improve matching performance significantly

Tackling in-camera downsizing for reliable camera id verification

Media Watermarking, Security, and Forensics 2019The photo-response non-uniformity (PRNU) of an imaging sensor can be regarded as a biometric identifier unique to each camera. This modality is referred to as camera ID. The underlying process for estimating and matching camera IDs is now well established, and its robustness has been studied under a variety of processing. However, the effect of in-camera downsizing on camera ID verification has not yet been methodologically addressed. In this work, we investigate limitations imposed by built-in camera downsizing methods and tackle the question of how to obtain a camera ID so that attribution is possible with lower resolution media. For this purpose, we developed an application that gathers photos and videos at all supported resolutions by controlling camera settings. Analysis of media obtained from 21 smartphone and tablet cameras shows that downsizing of photos by a factor of 4 or higher suppresses PRNU pattern significantly. On the contrary, it is observed that source of unstabilized videos can be verified quite reliably at almost all resolutions. We combined our observations in a camera ID verification procedure considering downsized media