Abstract: Due to the exponential disparity in the magnitude of high- and low-frequency components in the image frequency domain, existing frequency-domain enhancement methods for adversarial examples ...
Abstract: Even though thermostatically controlled loads like air conditioners present a great potential for providing ancillary services to the electric power grid, the practical challenges associated ...
Abstract: Recent studies have shown that Deep Neural Networks (DNNs) are easily deceived by adversarial examples, revealing their serious vulnerability. Due to the transferability, adversarial ...
Abstract: Deep neural networks are known to be susceptible to imperceptible adversarial perturbations. Many studies aim to interpret adversarial examples in the frequency domain. However, existing ...