da Silva, Murilo VargesMarana, Aparecido Nilceu [UNESP]2019-10-062019-10-062019-01-01Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 11401 LNCS, p. 547-555.1611-33490302-9743http://hdl.handle.net/11449/190196With the increasing use of social networks and mobile devices, the number of videos posted on the Internet is growing exponentially. Among the inappropriate contents published on the Internet, pornography is one of the most worrying as it can be accessed by teens and children. Two spatiotemporal CNNs, VGG-C3D CNN and ResNet R (2+1) D CNN, were assessed for pornography detection in videos in the present study. Experimental results using the Pornography-800 dataset showed that these spatiotemporal CNNs performed better than some state-of-the-art methods based on bag of visual words and are competitive with other CNN-based approaches, reaching accuracy of 95.1%.547-555eng3D CNNPornography detectionSpatiotemporal CNNVideo classificationTrabalho apresentado em evento10.1007/978-3-030-13469-3_64Acesso aberto2-s2.0-85063041642