Spotting Audio-Visual Inconsistencies (SAVI) in Manipulated Video.

This paper(1) is part of a larger effort to detect manipulations of video by searching for and combining the evidence of multiple types of inconsistencies between the audio and visual channels. Here, we focus on inconsistencies between the type of scenes detected in the audio and visual modalities (e.g., audio indoor, small room versus visual outdoor, urban), and inconsistencies in speaker identity tracking over a video given audio speaker features and visual face features (e.g., a voice change, but no talking face change). The scene inconsistency task was complicated by mismatches in the categories used in current visual scene and audio scene collections. To deal with this, we employed a novel semantic mapping method. The speaker identity inconsistency process was challenged by the complexity of comparing face tracks and audio speech clusters, requiring a novel method of fusing these two sources. Our progress on both tasks was demonstrated on two collections of tampered videos.