The political concept of “dogwhistling” in English is the use of language by politicians to use the same utterances to send different intended messages to different groups, e.g., to mislead the media about what they mean, while signalling a “real” message to a base of core followers “in the know”, so to speak.
A similar strategy is represented by the popular board game “Dixit”, where players are rewarded for deceiving SOME of their competitors about the identity of an abstract image BUT NOT ALL of their competitors – indeed, they are penalized if they deceive none OR ALL of their competitors.
This project is about taking the first steps to learn models of what might be called “adversarial communication” using human-identified image features. We will design an online interface intended to collect such features in the context of a game against a computer using a generative adversarial network (GAN) or reinforcement learning strategy, as appropriate.