Adversarial Attack
A security attack technique that causes machine learning models to produce erroneous outputs by adding small perturbations to input data. The two principal categories are white-box attacks, in which the attacker has access to the model's internal structure, and black-box attacks, in which the attacker can only observe model outputs.
Timeline (last 90 days)
The adversarial attack phenomenon was first discovered by Szegedy et al. in 2013, after which Goodfellow et al. proposed classic attack methods such as FGSM.
The core principle of adversarial example attacks is to apply imperceptibly small perturbations to input data that are nearly undetectable to the human eye, causing a trained deep learning model to produce completely incorrect outputs.
All Facts (2)
The core principle of adversarial example attacks is to apply imperceptibly small perturbations to input data that are nearly undetectable to the human eye, causing a trained deep learning model to produce completely incorrect outputs.
70%UnverifiedThe adversarial attack phenomenon was first discovered by Szegedy et al. in 2013, after which Goodfellow et al. proposed classic attack methods such as FGSM.
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