Learning under $p$-Tampering Attacks

Understand

Recently, Mahloujifar and Mahmoody (TCC'17) studied attacks against learning algorithms using a special case of Valiant's malicious noise, called $p$-tampering, in which the adversary gets to change any training example with independent probability $p$ but is limited to only choose malicious examples with correct labels.

• They obtained $p$-tampering attacks that increase the error probability in the so called targeted poisoning model in which the adversary's goal is to increase the loss of the trained hypothesis over a particular test example.
• At the heart of their attack was an efficient algorithm to bias the expected value of any bounded real-output function through $p$-tampering.
• In this work, we present new biasing attacks for increasing the expected value of bounded real-valued functions.