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Adversarial Example is an input deliberately crafted with small, often imperceptible changes that cause an AI model to make a confident but wrong prediction.
Adversarial Example, an input deliberately crafted with small, often imperceptible changes that cause an AI model to make a confident but wrong prediction.
Adversarial examples exploit the gap between how a model "sees" data and how a human does, a tiny, targeted perturbation can flip a classification while looking unchanged to a person. They are a core concern for the robustness and security of high-stakes AI, and a reason adversarial testing is part of serious model evaluation.
Source: Machine-learning security research
Adversarial examples exploit the gap between how a model "sees" data and how a human does, a tiny, targeted perturbation can flip a classification while looking unchanged to a person. They are a core concern for the robustness and security of high-stakes AI, and a reason adversarial testing is part of serious model evaluation.
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