This paper considers perception-driven control of a mobile robot for reference tracking where perception is performed by a machine learning system. The robot is subject to passive attacks and evasion attacks on image transmission. A robust output feedback controller together with a chaotic encryption system ensures input-to-state stability of the closed-loop system, and the chaotic encryption approach keeps image transmission secure. Simulations are conducted in the CARLA simulator to demonstrate robust reference tracking and secure image transmission.