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.

中文翻译：

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基于未抽取双树复小波变换和贝叶斯自适应直接搜索算法的稳定视频源识别方法

成像设备中广泛采用的视频稳定技术已成为限制基于光响应非均匀（PRNU）噪声的源相机识别（SCI）性能的瓶颈。稳定视频中的仿射变换（包括缩放、平移和旋转）会给跨帧的 PRNU 模式引入不同程度的失真，从而导致 SCI 方法中的准确性下降和匹配时间延长。为了解决这些问题，我们提出了一种稳定的视频源识别方法，该方法将未抽取双树复小波变换（UDTCWT）与贝叶斯自适应直接搜索（BADS）算法相结合。首先，通过跳过视频编解码器中的环路滤波器来获得解码帧，以保留更多的 PRNU。然后，利用基于UDTCWT的PRNU提取算法从解码帧中过滤出更可靠的PRNU。最后，利用BADS的仿射变换模型对失真的PRNU进行优化和恢复。在公共数据集上的实验表明，所提出的方法在匹配速度和准确性方面优于最先进的方法。