  As deep learning systems are widely adopted in safety- and security-critical
applications, such as autonomous vehicles, banking systems, etc., malicious
faults and attacks become a tremendous concern, which potentially could lead to
catastrophic consequences. In this paper, we initiate the first study of
leveraging physical fault injection attacks on Deep Neural Networks (DNNs), by
using laser injection technique on embedded systems. In particular, our
exploratory study targets four widely used activation functions in DNNs
development, that are the general main building block of DNNs that creates
non-linear behaviors -- ReLu, softmax, sigmoid, and tanh. Our results show that
by targeting these functions, it is possible to achieve a misclassification by
injecting faults into the hidden layer of the network. Such result can have
practical implications for real-world applications, where faults can be
introduced by simpler means (such as altering the supply voltage).
