Automatically Learning How to Evade Censorship

Researchers and censoring regimes have long engaged in a cat-and-mouse game, leading to increasingly sophisticated Internet-scale censorship techniques and methods to evade them. This talk will introduce a drastic departure from the previously manual evade-detect cycle: applying artificial intelligence techniques to automate the discovery of censorship evasion strategies. We will demonstrate that, by training AI against live censors, one can glean new insights into how censorship works around the world, and how to circumvent it. After a brief demonstration of a proof of concept involving genetic algorithms, the bulk of the talk will focus on future directions and open questions, including: Does automating the evade/detect cycle ultimately benefit the censor? What protocols can be automatically learned? And, can training be collected from many users and vantage points?