  Research on the detection of AI-generated videos has focused almost
exclusively on face videos, usually referred to as deepfakes. Manipulations
like face swapping, face reenactment and expression manipulation have been the
subject of an intense research with the development of a number of efficient
tools to distinguish artificial videos from genuine ones. Much less attention
has been paid to the detection of artificial non-facial videos. Yet, new tools
for the generation of such kind of videos are being developed at a fast pace
and will soon reach the quality level of deepfake videos. The goal of this
paper is to investigate the detectability of a new kind of AI-generated videos
framing driving street sequences (here referred to as DeepStreets videos),
which, by their nature, can not be analysed with the same tools used for facial
deepfakes. Specifically, we present a simple frame-based detector, achieving
very good performance on state-of-the-art DeepStreets videos generated by the
Vid2vid architecture. Noticeably, the detector retains very good performance on
compressed videos, even when the compression level used during training does
not match that used for the test videos.
