Deep neural network models perform well on image classification tasks, but they are highly susceptible to adversarial samples that add tiny perturbations and output incorrect classification results. The existence of adversarial samples seriously threatens the security of the model itself, in order to mitigate the threat posed by adversarial samples, researchers and scholars have proposed a number ...Show More
With the wide application of deep neural networks, the security problem of the model is becoming more prominent, adversarial attack is an important tool for evaluating the robustness and security of the model, adversarial attack can be categorized into white-box and black-box attacks. Aiming at the problem of huge perturbation and the low success rate of the adversarial example created in the tran...Show More
This paper presents a new approach to automatic modulation classification of cochannel signals based on cumulants. Unlike the case of single signal modulation classification, the presence of cochannel interference may render the computation of cumulant inaccurate, which therefore leads to poor modulation classification performance if the conventional cumulant based approaches are used. The main co...Show More
