Exposing frame deletion by detecting abrupt changes in video streams

Many existing methods for frame deletion detection attempt to detect abnormal periodical artifacts in video stream, however, due to a number of reasons, the periodical artifacts can not always be reliably detected. In this paper, we propose a new method for frame deletion detection. Rather than detecting abnormal periodical artifacts, we devise two features to measure the magnitude of variation in prediction residual and the number of intra macro blocks. Based on the devised features, we propose a fused index to capture abnormal abrupt changes in video streams. We create a dataset which consists of 6 subsets, and test the detection capability of our method in both video level and GOP (Group of Pictures) level. The experimental results show that the proposed method performs stably under various configurations.