This paper presents an algorithm to solve the problem of Photo-Response Non-Uniformity(PRNU)noise facing stabilized video.The stabilized video undergoes in-camera processing like rolling shutter correction.Thus,misali...This paper presents an algorithm to solve the problem of Photo-Response Non-Uniformity(PRNU)noise facing stabilized video.The stabilized video undergoes in-camera processing like rolling shutter correction.Thus,misalignment exists between the PRNU noises in the adjacent frames owing to the global and local frame registration performed by the in-camera processing.The misalignment makes the reference PRNU noise and the test PRNU noise unable to extract and match accurately.We design a computing method of maximum likelihood estimation algorithm for extracting the PRNU noise from stabilized video frames.Besides,unlike most prior arts tending to match the PRNU noise in whole frame,we propose a new patch-based matching strategy,aiming at reducing the influence from misalignment of frame the PRNU noise.After extracting the reference PRNU noise and the test PRNU noise,this paper adopts the reference and the test PRNU overlapping patch-based matching.It is different from the traditional matching method.This paper conducts different experiments on 224 stabilized videos taken by 13 smartphones in the VISION database.The area under curve of the algorithm proposed in this paper is 0.841,which is significantly higher than 0.805 of the whole frame matching in the traditional algorithm.Experimental results show good performance and effectiveness the proposed strategy by comparing with the prior arts.展开更多
文摘数字图像后处理流程带来的非唯一性人造(Non-Unique Artifacts,NUAs)噪声掺杂在具有唯一性、稳定性的光响应非均质性(Photo-Response Non-Uniformity,PRNU)指纹中,极大地影响了下游成像设备溯源任务的精确性。然而,现有NUAs抑制方案主要针对实验环境,不仅需要额外的超参数设定,而且需额外的算力和存储空间,难以在开放环境中实际应用。为解决该问题,提出了一种面向开放环境的PRNU指纹提纯算法。首先,对现有PRNU指纹相关性度量指标即峰值相关能量比(Peak-to-Correlation Energy Ratio,PCE)进行改进,提出了基于归一化的PCE_norm和PCE_denuas,以实现开放环境下的自适应相关性度量。然后,通过构建对比学习机制缩小同一指纹和放大不同指纹的距离,实现NUAs离线抑制,从而在溯源任务中不需额外计算和存储成本进行在线抑制。最后,通过在Dresden和Daxing数据集上的实验证明了所提算法的有效性和鲁棒性。
基金funded by the National Natural Science Foundation of China(61872203 and 61802212)the Shandong Provincial Natural Science Foundation(ZR2019BF017)+3 种基金Major Scientific and Technological Innovation Projects of Shandong Province(2019JZZY010127,2019JZZY010132,and 2019JZZY010201)Jinan City“20 universities”Funding Projects Introducing Innovation Team Program(2019GXRC031)Plan of Youth Innovation Team Development of colleges and universities in Shandong Province(SD2019-161)the Project of Shandong Province Higher Educational Science and Technology Program(J18KA331).
文摘This paper presents an algorithm to solve the problem of Photo-Response Non-Uniformity(PRNU)noise facing stabilized video.The stabilized video undergoes in-camera processing like rolling shutter correction.Thus,misalignment exists between the PRNU noises in the adjacent frames owing to the global and local frame registration performed by the in-camera processing.The misalignment makes the reference PRNU noise and the test PRNU noise unable to extract and match accurately.We design a computing method of maximum likelihood estimation algorithm for extracting the PRNU noise from stabilized video frames.Besides,unlike most prior arts tending to match the PRNU noise in whole frame,we propose a new patch-based matching strategy,aiming at reducing the influence from misalignment of frame the PRNU noise.After extracting the reference PRNU noise and the test PRNU noise,this paper adopts the reference and the test PRNU overlapping patch-based matching.It is different from the traditional matching method.This paper conducts different experiments on 224 stabilized videos taken by 13 smartphones in the VISION database.The area under curve of the algorithm proposed in this paper is 0.841,which is significantly higher than 0.805 of the whole frame matching in the traditional algorithm.Experimental results show good performance and effectiveness the proposed strategy by comparing with the prior arts.
基金the National Natural Science Foundation of China(41275037)the Science Fund for Distinguished Young Scholars of Anhui,China(1308085JGD03)the Anhui Province Natural Science Foundation of China(1308085QF124)
