In plain words: A neural network listens to audio and decides whether a recording is pornographic, so it can be filtered to protect children. Tested on a large audio collection, the base model reached 92.46% accuracy, and stacking all its tweaks raised that to 97.19%.
Abstract · Acoustic Pornography Recognition Using Convolutional Neural Networks and Bag of Refinements
A large number of pornographic audios publicly available on the Internet seriously threaten the mental and physical health of children, but these audios are rarely detected and filtered. In this paper, we firstly propose a convolutional neural networks (CNN) based model for acoustic pornography recognition. Then, we research a collection of refinements and verify their effectiveness through ablation studies. Finally, we stack all refinements together to verify whether they can further improve the accuracy of the model. Experimental results on our newly-collected large dataset consisting of 224127 pornographic audios and 274206 normal samples demonstrate the effectiveness of our proposed model and these refinements. Specifically, the proposed model achieves an accuracy of 92.46% and the accuracy is further improved to 97.19% when all refinements are combined.
Lifeng Zhou, Kaifeng Wei, Yuke Li, Yiya Hao, Weiqiang Yang, Haoqi Zhu
arXiv:2211.05983 · cs.SD, eess.AS · submitted Nov 11, 2022
abstract · pdf · html
First sentence. This is either a great humanitarian scouring for the victims of child porn. Or it is a useless puritanical crusade against porn.
I started reading the paper and this is just about porn filtering, pretty much useless perhaps even harmful.
I am sure using CNNs to do this is innovative, but if you are going to start from a lie why should I trust your research?