A Separation Result Between Data-oblivious and Data-aware Poisoning Attacks

Understand

Poisoning attacks have emerged as a significant security threat to machine learning algorithms.

• It has been demonstrated that adversaries who make small changes to the training set, such as adding specially crafted data points, can hurt the performance of the output model.
• Some of the stronger poisoning attacks require the full knowledge of the training data.
• This leaves open the possibility of achieving the same attack results using poisoning attacks that do not have the full knowledge of the clean training set.