Captcha automated assistant

A method implemented by one or more processors for using a completely automated public Turing test (“CAPTCHA”) to determine whether an incoming communication from a remote entity is computer-generated or human- generated, the method comprising:

• generating a textual challenge phrase using a challenge generator machine learning model that is trained in tandem with an answer discriminator machine learning model to generate textual challenges phrases that are configured to elicit a response indicative of whether the remote entity is human or synthetic;
• providing the textual challenge phrase to the remote entity;
• receiving, in response to providing the textual challenge phrase, an answer from the remote entity;
• assigning data indicative of the answer as input across the answer discriminator machine learning model to generate a CAPTCHA prediction, wherein the answer discriminator machine learning model is trained to generate the CAPTCHA prediction by:
• providing the answer discriminator machine learning model with synthetic answers and human answers, wherein the synthetic answers include labels that identify them as synthetic and the human answers include labels that identify them as human-generated,
• comparing CAPTCHA predictions output by the answer discriminator machine learning model with the labels, and
• training the answer discriminator machine learning model based on the comparing;
• determining, based on output generated by the answer discriminator machine learning model, a CAPTCHA prediction of whether the answer was computer-generated or human-generated;
• classifying the remote entity as synthetic based on the CAPTCHA prediction;
• and
• based on the classification of the remote entity as synthetic, preventing subsequent dialog with the remote entity.

A system for using a completely automated public Turing test (“CAPTCHA”) to classify a remote entity as malicious, the system including one or more processors to:

• generate a textual challenge phrase using a challenge generator machine learning model, wherein the textual challenge phrase includes a challenge configured to elicit a response from a natural language dialog software agent;
• transmit the textual challenge phrase to the remote entity over one or more computing networks;
• receive an answer from the remote entity over the one or more computing networks;
• apply data indicative of the answer across a trained discriminator machine learning model to generate output, wherein the discriminator machine learning model is trained in tandem with the challenge generator machine learning model;
• and
• classify the remote entity as malicious based on the output.