A Comparative Study of In-Domain vs Cross-Domain Learning for Porn Cartoon Classification

Abstract

Detection of adult contents such as pornography, sex, and nudity has been investigated extensively in the literature. Recently, content moderator is a significant component for social platforms to be integrated in their software applications and services. Cartoon content moderator is a specific kind of moderators that should be highly accurate to reduce the classification error and increase the model’s sensitivity to adult contents. This paper aims to compare the models pre-trained on natural adult images and called cross-domain learning models with ones pre-trained on cartoon images and called in-domain learning models for adult content detection in cartoons. The paper utilized pre-trained convolutional neural networks such as ResNet and EfficientNet to extract features that were applied to support vector machine for porn/normal classification. It was found that in-domain models outperformed cross-domain model in terms of performance metrics to improve the accuracy by 13 %, recall by 2 %, precision by 18 %, F1 score by 14 %, false negative rate by 2 %, and false positive rate by 16 %.