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 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 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.

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来源相机验证，用于高度稳定的视频

在成像和/或后处理过程中执行的图像稳定对来自视频的基于光响应非均匀性的源摄像机归因提出了最重大的挑战之一。以数字方式执行时，稳定涉及对视频帧进行裁剪，扭曲和修复，以消除不必要的摄像机运动。因此，成功的归因要求盲目地反转这些转换。为了解决这一挑战，我们针对视频引入了一种源摄像机验证方法，该方法考虑到了稳定变换的空间变异性，并假设其搜索具有更大的自由度。我们的方法在子帧级别识别变换，并结合了许多约束条件以验证其正确性，并在搜索正确变换时提供了计算灵活性。该方法还采用整体方法来抵抗其他视频生成步骤的破坏性影响，例如视频编码和缩小尺寸，以实现更可靠的归因。对一个公共数据集和两个自定义数据集进行的测试表明，该方法能够验证所有视频中23-30％的视频源（取决于计算量）是否具有更强的稳定性，而不会对虚假归因产生重大影响。