Multiparty adversarial neural cryptography with symmetric and asymmetric encryption

Abstract: Deep learning has shown excellent performance in image recognition, speech recognition, natural language processing and other fields over the recent decades. Cryptography is a technical science that studies the preparation and decoding of ciphers. With the development of artificial intelligence, people pay more and more attention to whether artificial intelligence can be applied to cryptography. A Google team designed a multiagent system a few years ago, which includes encrypting neural network, cracking network and eavesdropping network. Based on symmetric encryption, through deep learning training, the system achieves that the cracker can crack the encrypted text with minimal error and prevent the eavesdropper from cracking the plaintext. This research has aroused the interest of many scholars. Based on the research of the system, this thesis discusses the basic principle and related experiments of the system, as well as the design based on asymmetric encryption and the application in multiparty systems.