The detection of manipulated videos represents a highly relevant problem in multimedia forensics, which has been widely investigated in the last years. However, a common trait of published studies is the fact that the forensic analysis is typically applied on data prior to their potential dissemination over the web. This work addresses the challenging scenario where manipulated videos are first shared through social media platforms and then are subject to the forensic analysis. In this context, a large scale performance evaluation has been carried out involving general purpose deep networks and state-of-the-art manipulated data, and studying different effects. Results confirm that a performance drop is observed in every case when unseen shared data are tested by networks trained on non-shared data; however, fine-tuning operations can mitigate this problem. Also, we show that the output of differently trained networks can carry useful forensic information for the identification of the specific technique used for visual manipulation, both for shared and non-shared data.

Detection of manipulated face videos over social networks: A large-scale study / Marcon, F.; Pasquini, C.; Boato, G.. - In: JOURNAL OF IMAGING. - ISSN 2313-433X. - 7:10(2021), p. 193. [10.3390/jimaging7100193]

Detection of manipulated face videos over social networks: A large-scale study

Marcon F.;Pasquini C.;Boato G.
2021-01-01

Abstract

The detection of manipulated videos represents a highly relevant problem in multimedia forensics, which has been widely investigated in the last years. However, a common trait of published studies is the fact that the forensic analysis is typically applied on data prior to their potential dissemination over the web. This work addresses the challenging scenario where manipulated videos are first shared through social media platforms and then are subject to the forensic analysis. In this context, a large scale performance evaluation has been carried out involving general purpose deep networks and state-of-the-art manipulated data, and studying different effects. Results confirm that a performance drop is observed in every case when unseen shared data are tested by networks trained on non-shared data; however, fine-tuning operations can mitigate this problem. Also, we show that the output of differently trained networks can carry useful forensic information for the identification of the specific technique used for visual manipulation, both for shared and non-shared data.
2021
10
Marcon, F.; Pasquini, C.; Boato, G.
Detection of manipulated face videos over social networks: A large-scale study / Marcon, F.; Pasquini, C.; Boato, G.. - In: JOURNAL OF IMAGING. - ISSN 2313-433X. - 7:10(2021), p. 193. [10.3390/jimaging7100193]
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11572/328732
 Attenzione

Attenzione! I dati visualizzati non sono stati sottoposti a validazione da parte dell'ateneo

Citazioni
  • ???jsp.display-item.citation.pmc??? 1
  • Scopus 9
  • ???jsp.display-item.citation.isi??? 8
social impact