Violation Detection of Live Video Based on Deep Learning

Abstract EN

With the rapid development of Internet technology, live broadcast industry has also flourished.

However, in the public network live broadcast platform, live broadcast security issues have become increasingly prominent.

The detection of suspected pornographic videos in live broadcast platforms is still in the manual detection stage, that is, through the supervision of administrators and user reports.

At present, there are many online live broadcast platforms in China.

In mainstream live streaming platforms, the number of live broadcasters at the same time can reach more than 100,000 people/times.

Only through manual detection, there are a series of problems such as low efficiency, poor pertinence, and slow progress.

This approach is obviously not up to the task requirements of real-time network supervision.

For the identification of whether live broadcasts on the Internet contain pornographic content, a deep neural network model based on residual networks (ResNet-50) is proposed to detect pictures and videos in live broadcast platforms.

The core idea of detection is to classify each image in the video into two categories: (1) pass and (2) violation.

The experiments verify that the network proposed can heighten the efficiency of pornographic detection in webcasts.

The detection method proposed in this article can improve the accuracy of detection on the one hand and can standardize the detection indicators in the detection process on the other.

These detection indicators have a certain promotion effect on the classification of pornographic videos.