{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/spatiotemporal-cnns-for-pornography-detection","title":"Spatiotemporal CNNs for Pornography Detection in Videos","arxiv_id":"1810.10519","date":"2018-10-24","proceeding":null,"authors":["Murilo Varges da Silva","Aparecido Nilceu Marana"],"abstract":"With the increasing use of social networks and mobile devices, the number of\nvideos posted on the Internet is growing exponentially. Among the inappropriate\ncontents published on the Internet, pornography is one of the most worrying as\nit can be accessed by teens and children. Two spatiotemporal CNNs, VGG-C3D CNN\nand ResNet R(2+1)D CNN, were assessed for pornography detection in videos in\nthe present study. Experimental results using the Pornography-800 dataset\nshowed that these spatiotemporal CNNs performed better than some\nstate-of-the-art methods based on bag of visual words and are competitive with\nother CNN-based approaches, reaching accuracy of 95.1%.","url_abs":"http://arxiv.org/abs/1810.10519v1","url_pdf":"http://arxiv.org/pdf/1810.10519v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"spatiotemporal-cnns-for-pornography-detection","repo_url":"https://github.com/jackaduma/nude-detect","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"pornography-detection","task_name":"Pornography Detection"}],"methods":[{"method_slug":"2-1-d-convolution","method_name":"(2+1)D Convolution"},{"method_slug":"1x1-convolution","method_name":"1x1 Convolution"},{"method_slug":"average-pooling","method_name":"Average Pooling"},{"method_slug":"batch-normalization","method_name":"Batch Normalization"},{"method_slug":"bottleneck-residual-block","method_name":"Bottleneck Residual Block"},{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"dense-connections","method_name":"Dense Connections"},{"method_slug":"global-average-pooling","method_name":"Global Average Pooling"},{"method_slug":"kaiming-initialization","method_name":"Kaiming Initialization"},{"method_slug":"max-pooling","method_name":"Max Pooling"},{"method_slug":"r-2-1-d","method_name":"R(2+1)D"},{"method_slug":"relu","method_name":"ReLU"},{"method_slug":"residual-block","method_name":"Residual Block"},{"method_slug":"residual-connection","method_name":"Residual Connection"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}