  This paper presents a baseline approach and an experimental protocol for a
specific content verification problem: detecting discrepancies between the
audio and video modalities in multimedia content. We first design and optimize
an audio-visual scene classifier, to compare with existing classification
baselines that use both modalities. Then, by applying this classifier
separately to the audio and the visual modality, we can detect scene-class
inconsistencies between them. To facilitate further research and provide a
common evaluation platform, we introduce an experimental protocol and a
benchmark dataset simulating such inconsistencies. Our approach achieves
state-of-the-art results in scene classification and promising outcomes in
audio-visual discrepancies detection, highlighting its potential in content
verification applications.
