Character-Based Models for Adversarial Phone Extraction: Preventing Human Sex Trafficking

Nathanael Chambers, Timothy Forman, Catherine Griswold, Kevin Lu, Yogaish Khastgir, Stephen Steckler
US Naval Academy


Illicit activity on the Web often uses noisy text to obscure information between client and seller, such as the seller's phone number. This presents an interesting challenge to language understanding systems; how do we model adversarial noise in a text extraction system? This paper addresses the sex trafficking domain, and proposes some of the first neural network architectures to learn and extract phone numbers from noisy text. We create a new adversarial advertisement dataset, propose several RNN-based models to solve the problem, and most notably propose a visual character language model to interpret unseen unicode characters. We train a CRF jointly with a CNN to improve phone recognition by 89%. Through data augmentation in this unique model, we present the first results on noisy characters never seen in training.