US 2024/0403447 A1 · VARIANT INCONSISTENCY ATTACK (VIA) AS A SIMPLE AND EFFECTIVE ADVERSARIAL ATTACK METHOD

Abstract

The present disclosure relates to techniques for using variant inconsistency attack (VIA) as a simple and effective adversarial attack method to create useful adversarial examples for adversarial training of machine-learning models. In one particular aspect, a method is provided that includes obtaining a set of input examples for attacking a machine-learning model (the set of examples do not have corresponding labels), modifying an example from the set of examples in a utility preserving manner to generate a pair of modified examples, attacking the machine-learning model with the pair of modified examples in order generate a pair of predictions for the pair of modified examples, comparing the pair of predictions to determine whether the pair of predictions are the same or different, and in response to the pair of predictions being different, adding the pair of modified examples to a set of adversarial examples.

Claims20

Dependent claims

A computer implemented method comprising:

receiving, at a digital assistant comprising a chatbot, one or more utterances from a user, wherein the one or more utterances comprise at least one utterance having noise;
inputting the one or more utterances into the chatbot;
processing, by the chatbot, the one or more utterances, wherein the processing comprises:
performing, by a machine learning model, one or more natural language tasks on the one or more utterances to obtain output, wherein the machine learning model is fine-tuned on adversarial examples in order to perform the one or more natural language tasks on utterances that have noise, and
generating, based on the output, a natural language response to the one or more utterances;
and
communicating, by the digital assistant, the response to the user.

4 more elements

A system comprising:

one or more processors;
and
a memory coupled to the one or more processors, the memory storing a plurality of instructions executable by the one or more processors, the plurality of instructions comprising instructions that when executed by the one or more processors cause the one or more processors to perform processing comprising:
receiving, at a digital assistant comprising a chatbot, one or more utterances from a user, wherein the one or more utterances comprise at least one utterance having noise;
inputting the one or more utterances into the chatbot;
processing, by the chatbot, the one or more utterances, wherein the processing comprises:
performing, by a machine learning model, one or more natural language tasks on the one or more utterances to obtain output, wherein the machine learning model is fine-tuned on adversarial examples in order to perform the one or more natural language tasks on utterances that have noise, and
generating, based on the output, a natural language response to the one or more utterances;
and
communicating, by the digital assistant, the response to the user.

7 more elements

A non-transitory computer-readable memory storing a plurality of instructions executable by one or more processors, the plurality of instructions comprising instructions that when executed by the one or more processors cause the one or more processors to perform processing comprising:

receiving, at a digital assistant comprising a chatbot, one or more utterances from a user, wherein the one or more utterances comprise at least one utterance having noise;
inputting the one or more utterances into the chatbot;
processing, by the chatbot, the one or more utterances, wherein the processing comprises:
performing, by a machine learning model, one or more natural language tasks on the one or more utterances to obtain output, wherein the machine learning model is fine-tuned on adversarial examples in order to perform the one or more natural language tasks on utterances that have noise, and
generating, based on the output, a natural language response to the one or more utterances;
and
communicating, by the digital assistant, the response to the user.

4 more elements

Dependencies are inferred from the claim text.

Variant inconsistency attack (via) as a simple and effective adversarial attack method

Life of this patent

Variant inconsistency attack (via) as a simple and effective adversarial attack method

Life of this patent

Abstract

Claims20