Detecting Adversarial Advertisements in the Wild

In a large online advertising system, adversaries may attempt to profit from the creation of low quality or harmful
advertisements. In this paper, we present a large scale data
mining effort that detects and blocks such adversarial advertisements for the benefit and safety of our users. Because
both false positives and false negatives have high cost, our
deployed system uses a tiered strategy combining automated
and semi-automated methods to ensure reliable classification. We also employ strategies to address the challenges of
learning from highly skewed data at scale, allocating the effort of human experts, leveraging domain expert knowledge,
and independently assessing the effectiveness of our system.

• Data Mining and Modeling