The MULTI-Fake-DetectiVE challenge addresses the automatic detection of Italian fake news in a multimodal setting, where both textual and visual components contribute as potential sources of fake content. This paper describes the PoliTO approach to the tasks of fake news detection and analysis of the modality contributions. Our solution turns out to be the best performer on both tasks. It leverages the established FND-CLIP multimodal architecture and proposes ad hoc extensions including sentiment-based text encoding, image transformation in the frequency domain, and data augmentation via back-translation. Thanks to its effectiveness in combining visual and textual content, our solution contributes to fighting the spread of disinformation in the Italian news flow
