AI Model Defense Configuration Against Adversarial Attacks
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Solution Overview
Problem
Artificial Intelligence (AI) systems are vulnerable to adversarial attacks, which can deceive machine learning classifiers and compromise their accuracy, particularly in security-sensitive applications, with existing protection measures often failing to adequately safeguard against advanced attacks.
Innovation Solution
A computing device and method that identifies and evaluates combinations of protection measures for AI applications using machine learning algorithms to adjust AI models with computationally efficient defenses, optimizing them based on target configurations, metrics, and real-time threat assessments to enhance robustness against various adversarial attacks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple protection measures are applied against different attacks, then the robustness of the AI application is improved, but the computational complexity and resource consumption increase
Solution Approach 1:
The patent segments the protection measures into distinct categories (e.g., input preprocessing, model hardening, output post-processing) and evaluates them independently before combining. This allows the system to select specific segmentation elements based on the identified attack vectors, reducing unnecessary computational complexity while maintaining comprehensive protection.
Solution Approach 2:
Instead of applying all possible protection measures universally, the patent implements partial action by selecting only the necessary protection measures based on the vulnerability assessment. The system determines the minimum effective combination of protections required to address identified threats, avoiding excessive computational resource consumption.
2Reliability
If protection measures are applied to defend against adversarial attacks, then the accuracy under attack is improved, but the computational resources required increase
Solution Approach 1:
The patent performs preliminary vulnerability assessment and attack simulation before implementing full protection measures. By pre-identifying the most critical vulnerability vectors and testing protection effectiveness in advance, the system can optimize the combination of measures to achieve maximum accuracy protection with minimum computational resource consumption.
Solution Approach 2:
The system dynamically adjusts protection measure parameters based on the specific attack scenario and vulnerability profile. This includes modifying the strength and scope of protection measures according to the identified threat level, allowing the system to allocate computational resources efficiently while maintaining high accuracy under adversarial conditions.
3Adaptability or versatility
If a comprehensive set of protection measures is implemented, then the coverage of attack vectors is improved, but the time required for assessment and optimization increases
Solution Approach 1:
The patent segments attack vectors into distinct categories (e.g., data poisoning, adversarial examples, model extraction) and evaluates protection measures against each segment independently. This segmentation enables comprehensive coverage of all attack types while reducing assessment time through parallel evaluation of discrete protection components.
Solution Approach 2:
The system implements feedback mechanisms where the vulnerability assessment results feed back into the protection measure selection and optimization process. This iterative feedback loop allows the system to refine the protection combination based on actual performance data, reducing the time required for comprehensive assessment by learning from previous evaluations.
Data Source
AI summary
A method and system of protecting an artificial intelligence (AI) application are provided. Parameters of the AI application are identified. An assessment of a vulnerability of the AI application is performed, including: applying a combination of protection measures comprising two or more protection measures against at least two different attacks and at least one dataset, and determining whether the combination of protection measures is successful in defending the AI application. A target configuration of an AI model to protect the AI application is determined based on the assessed vulnerability of the AI application. An AI enhanced algorithm is determined to adjust the AI model to include a combination of most computationally efficient defenses based on the target configuration. The adjusted AI model is used to protect the AI application.