Synonyms: Adversarial Input, Adversarial Attack.
An adversarial attack is any perturbation of the input features or observations of a system (sometimes imperceptible to both humans and the own system) that makes the system fail or take the system to a dangerous state. A prototypical case of an adversarial situation happens with machine learning models, when an external agent maliciously modify input data –often in imperceptible ways– to induce them into misclassification or incorrect prediction. For instance, by undetectably altering a few pixels on a picture, an adversarial attacker can mislead a model into generating an incorrect output (like identifying a panda as a gibbon or a ‘stop’ sign as a ‘speed limit’ sign) with an extremely high confidence. While a good amount of attention has been paid to the risks that adversarial attacks pose in deep learning applications like computer vision, these kinds of perturbations are also effective across a vast range of machine learning techniques and uses such as spam filtering and malware detection. A different but related type of adversarial attack is called Data Poisoning, but this involves a malicious compromise of data sources (used for training or testing) at the point of collection and pre-processing.
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