Poisoning attacks corrupt the ML model during the data collection and model training stages of ML lifecycle. Poisoning refers to making imperceptible changes to training set. Attackers use poisoning attack to take control of an ML model by making imperceptible modifications to an existing training set, injecting new fake data into the training set, or tampering with the algorithm itself. For instance, attackers provide samples to the training set to shift the decision boundary in his or her favor. An adversary might input data during the training phase that is falsely labeled as harmless when it is actually malicious. Poisoning attacks are prevalent in online learning models which update their learning dynamically as new data come in.
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