The abundance of digital video forms a potential piece of evidence in courtrooms. Augmenting subjective assessment of digital video evidence by an automated objective assessment helps increase the accuracy of deciding whether or not to admit the digital video as legal evidence. This paper examines the authenticity of digital video evidence and in particular it proposes a machine learning approach to detecting frame deletion. A number of discriminative features are extracted from the video bit stream and its reconstructed images. The features are based on prediction residuals, percentage of intra-coded macroblocks, quantization scales and reconstruction quality. The importance of these features is verified by using stepwise regression. Consequently, the dimensionality of the feature vectors is reduced using spectral regression where it is shown that the projected features of unaltered and forged videos are nearly separable. Machine learning techniques are used to report the true positive and false negative rates of the proposed solution. It is shown that the proposed solution works for detecting forged videos regardless of the number of deleted frames, as long as it is not a multiple of the length of a group of pictures. It is also shown that the proposed solution is applicable for the two modes of video compression, variable and constant bitrate coding.