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

Source Camera Verification from Strongly Stabilized Videos

Image stabilization performed during imaging and/or post-processing poses one of the most significant challenges to photo-response non-uniformity based source camera attribution from videos. When performed digitally, stabilization involves cropping, warping, and inpainting of video frames to eliminate unwanted camera motion. Hence, successful attribution requires the inversion of these transformations in a blind manner. To address this challenge, we introduce a source camera verification method for videos that takes into account the spatially variant nature of stabilization transformations and assumes a larger degree of freedom in their search. Our method identifies transformations at a sub-frame level, incorporates a number of constraints to validate their correctness, and offers computational flexibility in the search for the correct transformation. The method also adopts a holistic approach in countering disruptive effects of other video generation steps, such as video coding and downsizing, for more reliable attribution. Tests performed on one public and two custom datasets show that the proposed method is able to verify the source of 23-30% of all videos that underwent stronger stabilization, depending on computation load, without a significant impact on false attribution

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