In important domains from natural resource conservation to
public safety, real-time information is becoming increasingly
important. Strategic deployment of security cameras and mobile sensors such as drones can provide real-time updates
on illegal activities. To help plan for such strategic deployments of sensors and human patrollers, as well as warning
signals to ward off adversaries, the defender-attacker security games framework can be used. [Zhang et al., 2019]
has shown that real-time data (e.g., human view from a helicopter) may be used in conjunction with security game
models to interdict criminals. Other recent work relies on
real-time information from sensors that can notify the patroller when an opponent is detected [Basilico et al., 2017;
Xu et al., 2018]. Despite considering real-time information
in all cases, these works do not consider the combined situation of uncertainty in real-time information in addition to
strategically signaling to adversaries. In this thesis, we will
not only address this gap, but also improve the overall security result by considering security game models and computer
vision algorithms together.