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.