Stabilized videos source identification method based on undecimated dual-tree complex wavelet transform and Bayesian adaptive direct search algorithm

Abstract

The widely adopted video stabilization techniques in imaging devices have become a bottleneck that limits the performance of source camera identification (SCI) based on photo-response non-uniform (PRNU) noise. Affine transformations in the stabilized video, including scaling, translation, and rotation, introduce varying degrees of distortion to the PRNU pattern across frames, which lead to a degradation of accuracy and prolonged matching time in SCI methods. To address these issues, we propose a stabilized video source identification method that combines the undecimated dual-tree complex wavelet transform (UDTCWT) with the Bayesian adaptive direct search (BADS) algorithm. First, decoded frames are obtained by skipping the loop filter in the video codec to preserve more PRNU. Then, a PRNU extraction algorithm based on the UDTCWT is utilized to filter out more reliable PRNU from the decoded frames. Finally, the distorted PRNU is optimized and recovered by the affine transformation model with the BADS. Experiments on public datasets show that the proposed method outperforms state-of-the-art methods in terms of matching speed and accuracy.

Double-weighted photo response non-uniformity extraction model based on multi-scale neighborhood shrinkage and structural filtering for YouTube compressed videos

“…They also proposed using scale-invariant feature transform features to gauge camera momentum and pinpoint less stable periods, thereby achieving more accurate identification analysis. Tian et al 21 proposed a stable video source identification methodology that combines undecimated DTCWT (UDTCWT) with the Bayesian adaptive direct search algorithm. This method solves the problem of different degrees of distortion caused by affine transformation to cross-frame PRNU, enhancing the precision and expediting the process of identifying the source camera.…”