Biblio

An adversarial attack is an algorithm that perturbs the input of a machine learning model in an intelligent way in order to change the output of the model. An important property of adversarial attacks is transferability. According to this property, it is possible to generate adversarial perturbations on one model and apply it the input to fool the output of a different model. Our work focuses on studying the transferability of adversarial attacks in sound event classification. We are able to demonstrate differences in transferability properties from those observed in computer vision. We show that dataset normalization techniques such as z-score normalization does not affect the transferability of adversarial attacks and we show that techniques such as knowledge distillation do not increase the transferability of attacks.

The local minima has been deemed as a challenging issue when designing routing protocols for 3D underwater sensor networks. Recently, harmonic potential field method has been used to tackle the issue of local minima which was also a major bottleneck in path planning and obstacle avoidance of robotics community. Inspired by this, this paper proposes a harmonic potential field based routing protocol for 3D underwater sensor networks with local minima. More specifically, the harmonic potential field is calculated using harmonic functions and Dirichlet boundary conditions are used for the local minima, sink(or seabuoy) and sending node. Numerical results show the effectiveness of the proposed routing protocol.