Pornographic Image Recognition Based on High and Low Level Feature Fusion with Human Body Masking and Attention
Authors: 
Yang Wang, Wengen LiAuthors Info & Claims
Yang Wang, Wengen LiAuthors Info & ClaimsPages 199 - 204
Abstract
Pornographic image recognition is an important task for Internet content providers, especially for the video streaming service providers such as Bilibili, Tiktok and Tencent. However, most of existing pornographic image recognition works treat this task as a binary classification problem, which cannot achieve satisfactory performance in practice. In this paper, we develop a simple yet effective method to recognize pornographic images. First, we construct a dataset for model training and testing via collaboration with a famous video streaming service provider. Different from existing studies, our dataset consists of six classes and 14,885 images, of which 3627 are pornographic images. Then, considering that the focus of pornographic image recognition is human body, we employ mask-rcnn to mask the human bodies in images, and highlight the body areas by attention mechanism. Furthermore, we fuse both high-level and low-level features of bodies for classification. Here, the high-level features are extracted by a ResNet, and the low-level features are extracted by a Spatial Pyramid Pooling network SPP-net. Experiments on our dataset show that our proposed method performs better than the existing methods and achieves a precision of 82.7%. More importantly, our method has been applied to the pornographic image monitoring platform of the famous video streaming service provider mentioned above.
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- Pornographic Image Recognition Based on High and Low Level Feature Fusion with Human Body Masking and Attention
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August 2022
253 pages
ISBN:9781450396851
DOI:10.1145/3562007
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Published: 12 October 2022
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CCRIS'22
CCRIS'22: 2022 3rd International Conference on Control, Robotics and Intelligent System
August 26 - 28, 2022
Virtual Event, China
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