Apostolidis, KonstantinosKonstantinosApostolidisAbeßer, JakobJakobAbeßerCuccovillo, LucaLucaCuccovilloVasileios, MezarisMezarisVasileios2024-08-072024-08-072024-08-072024https://publica.fraunhofer.de/handle/publica/47288810.1145/3643491.3660287This 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.enMedia Forensicsaudio-visual forensicsaudio-visual scene classificationcontent verificationself-attentionEnvironmental Sound Analysisconference paper