title: Codec and GOP Identification in Double Compressed Videos.
abstract: Video content is routinely acquired and distributed in a digital compressed format. In many cases, the same video content is encoded multiple times. This is the typical scenario that arises when a video, originally encoded directly by the acquisition device, is then re-encoded, either after an editing operation, or when uploaded to a sharing website. The analysis of the bitstream reveals details of the last compression step (i.e., the codec adopted and the corresponding encoding parameters), while masking the previous compression history. Therefore, in this paper, we consider a processing chain of two coding steps, and we propose a method that exploits coding-based footprints to identify both the codec and the size of the group of pictures (GOPs) used in the first coding step. This sort of analysis is useful in video forensics, when the analyst is interested in determining the characteristics of the originating source device, and in video quality assessment, since quality is determined by the whole compression history. The proposed method relies on the fact that lossy coding is an (almost) idempotent operation. That is, re-encoding a video sequence with the same codec and coding parameters produces a sequence that is similar to the former. As a consequence, if the second codec in the chain does not significantly alter the sequence, it is possible to analyze this sort of similarity to identify the first codec and the adopted GOP size. The method was extensively validated on a very large data set of video sequences generated by encoding content with a diversity of codecs (MPEG-2, MPEG-4, H.264/AVC, and DIRAC) and different encoding parameters. In addition, a proof of concept showing that the proposed method can also be used on videos downloaded from YouTube is reported.
authors.forename: Paolo
authors.initials: P
authors.lastname: Bestagini
authors.forename: Simone
authors.initials: S
authors.lastname: Milani
authors.forename: Marco
authors.initials: M
authors.lastname: Tagliasacchi
authors.forename: Stefano
authors.initials: S
authors.lastname: Tubaro