National College Student Information Security Contest

Contest, Wuhan University, 2017

In recent years, with the vigorous development of the Internet, social networks have become a necessity for every one of us. We often use instant messaging tools such as WeChat to make video calls and use platforms such as Weibo and Friendship Circle to record our lives. But as we enjoy the fun and convenience of social networks and instant messaging devices, our personal information is slowly leaking through these modern communication tools. Because of the openness of social networks and video communication platforms, when we use videos or photos containing personal portrait information on the network, we cannot ensure that this facial information is not abused, nor can we rely entirely on the Internet platform to protect our information.

At present, many face-based authentication systems still use 2D devices to identify users, which means that our photos and videos and other 2D portrait information can be easily used in these authentication systems. Taking Android mobile phone as an example, there are few devices using 3D depth camera to obtain relatively safe three-dimensional facial data. Most of them still use ordinary cameras for facial recognition, which facilitates the abuse of portrait information.

In order to solve this problem, we are committed to propose a solution to protect the face information flooded in social networks and video communications. The face processing of real-time video calls or photos greatly reduces the possibility of the image being verified by the identity authentication system directly and does not affect the view of the photos or videos themselves. In view of the rapid development of mobile devices and applications, our solution will use Android applications as a platform to provide a facial information protection tool for the vast number of social media users.

In order to achieve this product, we choose to add noise to the video or photo to make the direct 2D face recognition fail, which cannot be verified by the face recognition system, and reduce the impact of noise to the unaware of the naked eye (see figure 2.6.2). The basis of face recognition is neural network training, as long as the face part of the photo cannot be recognized by the general neural network. On the other hand, for the noisy images needed in image processing, we train them by deep learning. By building the training model of noise image neural network and using the existing universal face data set as data input, the available noise image is generated according to the corresponding algorithm.