Detecting and Combating Deep Fakes

Art Fakes – What Fakes?, An Economic View

An economic analysis of fakes differs substantially from the legal and the art historic views which both tend to propose repressive policies against imitations. But there are large economic benefits from copying. In particular, the "propagation effect" yields utility to both consumers and creators of the original. In contrast, the harmful effects of fakes are small and are mitigated by emerging...

Combating Human Trafficking with Deep Multimodal Models

Human trafficking is a global epidemic affecting millions of people across the planet. Sex trafficking, the dominant form of human trafficking, has seen a significant rise mostly due to the abundance of escort websites, where human traffickers can openly advertise among at-will escort advertisements. In this paper, we take a major step in the automatic detection of advertisements suspected to p...

Detecting Overlapping Communities in Social Networks using Deep Learning

In network analysis, a community is typically considered of as a group of nodes with a great density of edges among themselves and a low density of edges relative to other network parts. Detecting a community structure is important in any network analysis task, especially for revealing patterns between specified nodes. There is a variety of approaches presented in the literature for overlapping...

Combating Deep Reinforcement Learning’s Sisyphean Curse with Intrinsic Fear

To use deep reinforcement learning in the wild, we might hope for an agent that can avoid catastrophic mistakes. Unfortunately, even in simple environments, the popular deep Q-network (DQN) algorithm is doomed by a Sisyphean curse. Owing to the use of function approximation, these agents eventually forget experiences as they become exceedingly unlikely under a new policy. Consequently, for as l...

Non-specific binding of antibodies in immunohistochemistry: Fakes and facts