A video can be manipulated using synthetic zooming without using the state-of-the-art video forgeries. Synthetic zooming is performed by upscaling individual frames of a video with varying scale factors followed by cropping them to the original frame size. These manipulated frames resemble genuine natural (optical) camera zoomed frames and hence may be misclassified as a pristine video by video forgery detection algorithms. Even if such a video is classified as forged, forensic investigators may ignore the results, believing it as part of an optical camera zooming activity. Hence, this can be used as an anti-forensic method which eliminates digital evidence. In this paper, we propose a method for differentiating optical camera zooming from synthetic zooming for video tampering detection. The features used for this method are pixel variance correlation and sensor pattern noise. Experimental results on a dataset containing 3200 videos show the effectiveness of the proposed method.

可以使用合成缩放来操纵视频，而无需使用最新的视频伪造。合成缩放是通过以可变比例因子将视频的各个帧放大，然后将其裁剪为原始帧大小来执行的。这些操纵的帧类似于真正的自然（光学）相机变焦帧，因此，可能会被视频伪造检测算法误分类为原始视频。即使将此类视频归类为伪造视频，法医调查人员也可能会忽略结果，认为这是光学相机变焦活动的一部分。因此，这可以用作消除数字证据的取证方法。在本文中，我们提出了一种区分光学相机变焦与合成变焦以进行视频篡改检测的方法。此方法使用的功能是像素差异相关性和传感器图案噪声。在包含3200个视频的数据集上的实验结果表明了该方法的有效性。