AI Submodule Preventing Model Stealing via Output Comparison
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Solution Overview
Problem
Existing AI systems are vulnerable to model stealing attacks, where adversaries can capture and replicate AI models by sending iterative queries, leading to potential business disadvantages, loss of confidential information, and intellectual property theft.
Innovation Solution
The proposed solution involves an AI system architecture with a submodule that includes a blocker module to identify and prevent attack vectors by comparing outputs from multiple models, calculating information gain, and modifying outputs to disguise the trained model logic while maintaining classification accuracy, and a notification system to alert the owner of potential attacks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the AI system uses a single model for processing input data, then the system complexity is low and ease of operation is maintained, but the system becomes vulnerable to model stealing attacks where adversaries can capture and replicate the model logic
Solution Approach 1:
The AI system is segmented into multiple independent models (first model and second model) that process input data separately. The submodule receives input data and executes both models to generate outputs, comparing results to detect attack vectors. This segmentation prevents adversaries from capturing a single model's logic while maintaining overall system functionality through distributed processing.
Solution Approach 2:
A submodule acts as an intermediary between the input data and the output generation process. This submodule executes multiple models, compares their outputs, identifies attack vectors, and determines whether to block requests. The intermediary protects the core AI models from direct exposure to malicious queries while maintaining legitimate system operations.
2Reliability
If the system executes multiple models and compares outputs to identify attack vectors, then protection against model stealing attacks is improved, but the processing time and computational resources increase
Solution Approach 1:
The system performs preliminary actions by pre-training multiple models on the same training data before deployment. During operation, these pre-trained models can be executed in parallel to process input data, reducing the time penalty of running multiple models. The attack vector identification logic is also pre-established through the comparison mechanism, enabling rapid detection without extensive real-time computation.
Solution Approach 2:
The system applies partial action by selectively comparing model outputs only when necessary for attack detection, rather than performing exhaustive analysis on every input. The submodule determines whether to block requests based on comparison results, applying protection measures only when attack vectors are identified, thus optimizing the balance between security and processing efficiency.
3Reliability
If the system blocks malicious queries to prevent model capture, then intellectual property protection is improved, but the quantity of usable input data processed decreases
Solution Approach 1:
The system implements feedback mechanisms where the submodule continuously monitors model outputs, compares results, and learns from detected attack patterns. This feedback loop enables the system to refine its attack vector identification over time, improving the accuracy of blocking decisions. Legitimate queries that do not trigger attack detection are processed normally, maintaining productivity while protecting intellectual property through intelligent, adaptive filtering.
Data Source
AI summary
A method of training a submodule and preventing capture of an AI module is disclosed. Input data received from an input interface is transmitted through a blocker module to an AI module, which computes a first output data by executing a first model. A submodule in the AI system trained using methods steps processes the input data to identify an attack vector from the input data. The submodule executes the first model and at least a second model. The first model and the second model have a first and second set of network parameters and hyper-parameters respectively. The identification information of the attack vector is sent to the information gain module.