Protecting Personal Computers from Unauthorized Mobile Recordings
Unauthorized mobile screen recordings pose a serious threat to the security and privacy of personal computers in today's digital era. Nevertheless, there is a lack of prior research conducted to address this particular challenge. To tackle this challenge, we present a deep learning approach that effectively manipulates the channels in the temporal dimension in video frames. The channel manipulation in temporal dimension allows the mixing of feature maps from adjacent frames with the current frame, resulting in improved mobile action recognition in videos. Moreover, the Mobilenetv2 architecture incorporates the channel shifting module after the bypass connections. In addition, the proposed method employs the Mobilenetv2 architecture, resulting in improved computational efficiency for frame processing. Consequently, it is well-suited for real-time recognition of unauthorized mobile screen recording, with low latency.