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

Abstract

Photo response non-uniformity (PRNU), a unique trait of image sensors, can efficiently trace the camera source of a digital video. In response to the poor identification effect of network compression subsampling videos, a multi-scale neighborhood value shrinkage filtering algorithm based on Stein's unbiased risk estimation and an adaptive edge structure-preserving smoothing filtering algorithm are proposed, and a double-weighted PRNU extraction model is constructed. This model first performs a multi-scale transformation based on the dual-tree complex wavelet of the video frame that skips the loop filter, uses the multi-scale neighborhood value shrinkage filtering algorithm based on Stein's unbiased risk estimation to estimate all highfrequency sub-bands, obtains the noise residual, and uses the adaptive edge structure-preserving smoothing filter to smooth the complex noise residual. The maximum likelihood estimation method of the double-weight coefficient matrix based on the noise variance and quantization parameters is used to aggregate the noise residuals to obtain the PRNU. Verification is carried out on the public Vision database. The proposed model outperforms the current PRNU extraction model in source identification of compressed videos, as evidenced by the receiver operating characteristic curve, area under the curve, and Kappa coefficient values. It exhibits an accuracy rate that surpasses the best competing algorithm by 17.4%.