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Adversarial Examples and Robustness
Adversarial examples are subtly perturbed inputs, often imperceptible to humans, that cause AI models to misclassify with high confidence. Understanding how to generate and defend against these examples is crucial for building trustworthy AI. This field investigates the vulnerabilities and limits of current deep learning architectures.
In plain terms
Imagine a magician who can make you see a rabbit in a hat, even if it's actually an apple, just by changing a tiny detail you can't even notice.
Why it matters
Ensuring AI systems are robust against intentional attacks is vital for their deployment in sensitive applications like self-driving cars or medical diagnosis.
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