Social media and messaging apps have become major communication platforms. Multimedia contents promote improved user engagement and have thus become a very important communication tool. However, fake news and manipulated content can easily go viral, so, being able to verify the source of videos and images as well as to distinguish between native and downloaded content becomes essential. Most of the work performed so far on social media provenance has concentrated on images; in this paper, we propose a CNN architecture that analyzes video content to trace videos back to their social network of origin. The experiments demonstrate that stating platform provenance is possible for videos as well as images with very good accuracy.

Learning double-compression video fingerprints left from social-media platforms / Amerini, I.; Anagnostopoulos, A.; Maiano, L.; RICCIARDI CELSI, Lorenzo. - 2021-:(2021), pp. 2530-2534. (Intervento presentato al convegno ICASSP 2021 tenutosi a Toronto; Canada) [10.1109/ICASSP39728.2021.9413366].

Learning double-compression video fingerprints left from social-media platforms

Amerini I.
;
Anagnostopoulos A.
;
Maiano L.
;
RICCIARDI CELSI LORENZO
2021

Abstract

Social media and messaging apps have become major communication platforms. Multimedia contents promote improved user engagement and have thus become a very important communication tool. However, fake news and manipulated content can easily go viral, so, being able to verify the source of videos and images as well as to distinguish between native and downloaded content becomes essential. Most of the work performed so far on social media provenance has concentrated on images; in this paper, we propose a CNN architecture that analyzes video content to trace videos back to their social network of origin. The experiments demonstrate that stating platform provenance is possible for videos as well as images with very good accuracy.
2021
ICASSP 2021
Deep learning; Multitask learning; Platform provenance analysis; Social networks; Video forensics
04 Pubblicazione in atti di convegno::04b Atto di convegno in volume
Learning double-compression video fingerprints left from social-media platforms / Amerini, I.; Anagnostopoulos, A.; Maiano, L.; RICCIARDI CELSI, Lorenzo. - 2021-:(2021), pp. 2530-2534. (Intervento presentato al convegno ICASSP 2021 tenutosi a Toronto; Canada) [10.1109/ICASSP39728.2021.9413366].
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Note: DOI: 10.1109/ICASSP39728.2021.9413366
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1615509
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