  In e-commerce, product content, especially product images have a significant
influence on a customer's journey from product discovery to evaluation and
finally, purchase decision. Since many e-commerce retailers sell items from
other third-party marketplace sellers besides their own, the content published
by both internal and external content creators needs to be monitored and
enriched, wherever possible. Despite guidelines and warnings, product listings
that contain offensive and non-compliant images continue to enter catalogs.
Offensive and non-compliant content can include a wide range of objects, logos,
and banners conveying violent, sexually explicit, racist, or promotional
messages. Such images can severely damage the customer experience, lead to
legal issues, and erode the company brand. In this paper, we present a computer
vision driven offensive and non-compliant image detection system for extremely
large image datasets. This paper delves into the unique challenges of applying
deep learning to real-world product image data from retail world. We
demonstrate how we resolve a number of technical challenges such as lack of
training data, severe class imbalance, fine-grained class definitions etc.
using a number of practical yet unique technical strategies. Our system
combines state-of-the-art image classification and object detection techniques
with budgeted crowdsourcing to develop a solution customized for a massive,
diverse, and constantly evolving product catalog.
