  Deep neural networks (DNNs) have been widely used in many fields such as
images processing, speech recognition; however, they are vulnerable to
adversarial examples, and this is a security issue worthy of attention. Because
the training process of DNNs converge the loss by updating the weights along
the gradient descent direction, many gradient-based methods attempt to destroy
the DNN model by adding perturbations in the gradient direction. Unfortunately,
as the model is nonlinear in most cases, the addition of perturbations in the
gradient direction does not necessarily increase loss. Thus, we propose a
random directed attack (RDA) for generating adversarial examples in this paper.
Rather than limiting the gradient direction to generate an attack, RDA searches
the attack direction based on hill climbing and uses multiple strategies to
avoid local optima that cause attack failure. Compared with state-of-the-art
gradient-based methods, the attack performance of RDA is very competitive.
Moreover, RDA can attack without any internal knowledge of the model, and its
performance under black-box attack is similar to that of the white-box attack
in most cases, which is difficult to achieve using existing gradient-based
attack methods.
