Application of Image Classification for Fine-Grained Nudity Detection

Many online social platforms need to use an image content filtering solution to detect nudity automatically. Existing solutions only focus on binary classification models to detect explicit nudity or pornography, which is not enough to distinguish between a great number of various racy outfits that people wear (swimsuit, summer outfit, etc.) that might be considered inappropriate according to user's own preferences. This paper addresses the problem by proposing a robust technology which detects fine-grained human body parts (chest, back, abdomen, etc.) and assigns a level of nudity for each part using a multi-label image classification model. Since existing datasets were not sufficient for the given problem, we created a new dataset with a total of 37.872 images and 4.879.517 annotated labels that describe human body parts and nudity level (6 labels for each body part). We fine-tuned multiple state-of-the-art convolutional neural network models (VGG, ResNet, MobileNet) on our dataset for multi-label image classification. Our solution has a total accuracy of 98.1% on the test dataset and a low false positive rate of 0.8%.