From the course: Complete Guide to Cybersecurity: A Practical Approach

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Understanding robustness and resilience in AI models

Understanding robustness and resilience in AI models

From the course: Complete Guide to Cybersecurity: A Practical Approach

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Understanding robustness and resilience in AI models

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- So let's go over robustness in AI models. This is the ability for an AI model to maintain its performance when it is actually subject to different types of perturbations. That actually includes input data and permutations in the environment conditions and malicious attacks as well, right? Now, a robust AI model should be able to respond correctly to minor changes, right, whether it's a slight alteration to an image for vision models or subtle shifts in word usage for language models and so on. Or in the case, of course, adversarial attacks, resisting attempts to manipulate the model's outputs by tweaking inputs in a strategic manner by the, of course, attacker. And such attacks are intended to fool the model to making different errors in the output. And we cover that earlier. And then lastly, noise and errors. Basically dealing effectively with noise or errors in the data without significant drop in performance of the whole system, right? Now, resiliency in AI basically is the…

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