Convolutional neural network (CNN) models are typically composed of several gigabytes of data, requiring dedicated hardware and significant processing capabilities for proper handling. In addition, video-detection tasks are typically performed offline, and each video frame is analyzed individually, meaning that the video’s categorization (class assignment) as normal or pornographic is only complete after all the video frames have been evaluated. This paper proposes the Private Parts Censor (PPCensor), a CNN-based architecture for transparent and near real-time detection and obfuscation of pornographic video frame regions. Our contribution is two-fold. First, the proposed architecture is the first that addresses the detection of pornographic content as an object detection problem. The objective is to apply user-friendly content filtering such that an inevitable false positive will obfuscate only regions (objects) within the video frames instead of blocking the entire video. Second, the PPCensor architecture is deployed on dedicated hardware, and real-time detection is deployed using a video-oriented streaming proxy. If a pornographic video frame is identified in the video, the system can hide pornographic content (private parts) in real time without user interaction or additional processing on the user’s device. Based on more than 50,000 objects labeled manually, the evaluation results show that the PPCensor is capable of detecting private parts in near real time for video streaming. Compared to cutting-edge CNN architectures for image classification, PPCensor achieved similar results, but operated in real time. In addition, when deployed on a desktop computer, PPCensor handled up to 35 simultaneous connections without the need for additional processing on the end-user device.

卷积神经网络（CNN）模型通常由数GB的数据组成，需要专用硬件和重要的处理能力才能正确处理。此外，视频检测任务通常是在离线状态下执行的，每个视频帧都经过单独分析，这意味着只有在评估完所有视频帧之后，才将视频分类为正常或色情内容。本文提出了专用零件检查器（PPCensor），这是一种基于CNN的体系结构，用于透明和近实时地检测和模糊色情视频帧区域。我们的贡献是双重的。首先，所提出的架构是第一个解决色情内容检测问题的对象。目的是应用用户友好的内容过滤，以使不可避免的误报只会混淆视频帧内的区域（对象），而不是阻塞整个视频。其次，将PPCensor体系结构部署在专用硬件上，并使用面向视频的流代理来部署实时检测。如果在视频中标识了色情视频帧，则系统可以实时隐藏色情内容（私有部分），而无需用户交互或在用户设备上进行其他处理。通过对50,000多个手动标记的对象进行评估，评估结果表明，PPCensor能够实时检测视频流中的私有部分。与用于图像分类的尖端CNN架构相比，PPCensor取得了相似的结果，但是可以实时运行。