Resumo
Despite YouTube’s efforts to block violent and pornographic content from its platform, it is not prepared to deal with the Elsagate phenomenon. As a first to introduce the disturbing cartoons to the literature, we propose along with a dataset a pipeline that classifies Elsagate videos with 92.6% of accuracy and discuss the subjectivity of this problem.
Referências
Ishikawa, Akari, Edson Bollis, and Sandra Avila. "Combating the Elsagate phenomenon: Deep learning architectures for disturbing cartoons." arXiv preprint arXiv:1904.08910 (2019).
Perez, Mauricio, et al. "Video pornography detection through deep learning techniques and motion information." Neurocomputing 230 (2017): 279-293.
Perez, Mauricio, et al. "Video pornography detection through deep learning techniques and motion information." Neurocomputing 230 (2017): 279-293.
Todos os trabalhos são de acesso livre, sendo que a detenção dos direitos concedidos aos trabalhos são de propriedade da Revista dos Trabalhos de Iniciação Científica da UNICAMP.
Downloads
Não há dados estatísticos.