An approach to evaluate the resilience of AI models to adversarial attacks with property-based testing

When machine learning models are used in the real world they often run into problems with adversarial attacks. Generating unseen data that the model is expected to operate on is one method of strengthening the models. This problem can be resolved using property-based testing. Property-based testing can be used to spot and stop adversarial attacks on AI models by developing a variety of test cases. Property-based fuzzing, adversarial training and property-based verification are used in this research to construct an effective approach. We proposed an approach that outlines how property-based testing can make an AI model more resistant to attacks from the outside. This approach aims to strengthen an AI model’s resilience to sophisticated attacks and enhance its trustworthiness. By using property-based testing the framework can identify vulnerabilities in an AI model and provide a more comprehensive understanding of its behavior. This approach can also help to improve the overall quality of the AI model and increase its effectiveness in real world applications.