AI Query Screening Module for Model Stealing Prevention
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Solution Overview
Problem
Existing AI systems are vulnerable to model stealing attacks where adversaries capture AI models by sending iterative queries, leading to potential business disadvantages, loss of confidential information, and intellectual property theft.
Innovation Solution
A method for training AI modules using supervised learning to identify and classify good and bad data, balancing class imbalance, and employing an information gain mechanism to flag and lock out users or inputs exceeding a threshold to prevent model capture.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If AI systems process large amounts of data to train models, then model accuracy and intelligence are improved, but the system becomes vulnerable to model stealing attacks where adversaries can capture the model by sending iterative queries
Solution Approach 1:
The system performs preliminary actions by training a separate module before deployment to detect model stealing attacks. This module is trained with labeled data (good vs bad queries) and learns to identify adversarial patterns before they can compromise the main AI model, preventing model capture while maintaining the model's accuracy
Solution Approach 2:
A trained detection module acts as an intermediary between the AI model and incoming queries. This module analyzes queries before they reach the model and blocks suspicious ones, serving as a protective layer that prevents adversaries from stealing the model while allowing legitimate queries to pass through unchanged
2Reliability
If the system implements attack detection mechanisms, then model security is improved, but the system complexity increases
Solution Approach 1:
The system segments the security function into a separate, dedicated module that is independent from the main AI model. This module handles all attack detection and filtering operations, allowing the main model to remain simple and focused on its primary function while security concerns are addressed by the specialized detection component
Solution Approach 2:
Instead of modifying the main AI model to include security features, the system creates a copy or replica (the detection module) that mirrors the model's input interface but serves a different purpose. This copied module analyzes queries for malicious patterns without affecting the original model's structure or operation
3Loss of information
If the system blocks suspicious queries to prevent model capture, then intellectual property protection is improved, but the loss of legitimate queries increases
Solution Approach 1:
The detection module uses feedback from the labeling process and attack patterns to continuously improve its discrimination between malicious and legitimate queries. By learning from labeled examples of both good and bad queries, the system refines its detection accuracy over time, reducing false positives that would block legitimate users while maintaining strong protection against actual attacks
Data Source
AI summary
A method of training a module in an AI system and a method of preventing capture of an AI module in the AI system is disclosed. The AI system includes at least an AI module executing a model, a dataset, and the module adapted to be trained. The method includes receiving input data in the module adapted to be trained, labelling data as good data and bad data in the module adapted to be trained, classifying binarily the labelled good data and the labelled bad data in the module adapted to be trained, inputting the binarily classified data into the AI module, and recording internal behavior of the AI module in response to the binarily classified data on the module adapted to be trained.