Shanghai Jiaotong University

Guangtao Xue

Hao Pan

Yue Wu

Jianhua Li

Cunqing Hua

Futai Zou

Yichao Chen

Ping Yi

Weidong Qiu

Yongkang Jiang

The present disclosure provides a large-scale malware classification system, comprising a client for unloading malware and a server for receiving and classifying the malware, wherein the server comprises a deep learning module for classifying the malware.

Large-scale malware classification system

This invention relates to a secure QrCode communication method based on nonlinear spatial frequency characteristics, comprising: camera modeling: modeling according to the spatial frequency of the color filter matrix of the scanning device's camera; QrCode encryption: using the CFA spatial frequency of scanning device and modeling results, as well as the spatial frequency of the display device, generate an encrypted picture of the target QrCode on the display device; QrCode decryption: the camera of the scanning device takes the picture of the display at a specified position and a specified angle, and parses the image to recover the target QrCode. Compared with the prior art, the present invention utilizes the nonlinear characteristics of the optical spatial frequency, and uses the spatial frequency of the camera's own color filter array and the spatial frequency of the display to modulate the target two-dimensional code through phase to achieve the effect of encryption.

Secure QR code using nonlinearity of spatial frequency in light

Growing community of inventors

Shanghai, China

Guangtao Xue