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Security of deep neural network (DNN) inference engines, i.e., trained DNN models on various platforms, has become one of the biggest challenges in deploying artificial intelligence in domains where privacy, safety, and reliability are of paramount importance, such as in medical applications. In addition to classic software attacks such as model inversion and evasion attacks, recently a new attack surface---implementation attacks which include both passive side-channel attacks and active fault injection and adversarial attacks---is arising, targeting implementation peculiarities of DNN to breach their confidentiality and integrity. This paper presents several novel passive and active attacks on DNN we have developed and tested over medical datasets. Our new attacks reveal a largely under-explored attack surface of DNN inference engines. Insights gained during attack exploration will provide valuable guidance for effectively protecting DNN execution against reverse-engineering and integrity violations. 

Continuous trajectory control of fixed-wing unmanned aerial vehicles (UAVs) is complicated when considering hidden dynamics. Due to UAV multi degrees of freedom, tracking methodologies based on conventional control theory, such as Proportional-Integral-Derivative (PID) has limitations in response time and adjustment robustness, while a model based approach that calculates the force and torques based on UAV’s current status is complicated and rigid.We present an actor-critic reinforcement learning framework that controls UAV trajectory through a set of desired waypoints. A deep neural network is constructed to learn the optimal tracking policy and reinforcement learning is developed to optimize the resulting tracking scheme. The experimental results show that our proposed approach can achieve 58.14% less position error, 21.77% less system power consumption and 9:23% faster attainment than the baseline. The actor network consists of only linear operations, hence Field Programmable Gate Arrays (FPGA) based hardware acceleration can easily be designed for energy efficient real-time control.