Due to the importance of security, the adversarial attack has become an increasingly
popular area for deep learning, especially the black-box adversarial attack, which can only …

Most existing adversarial attack methods against detectors involve adding adversarial
perturbations to benign images to synthesize adversarial examples. However, directly …

The sparse adversarial attack has attracted increasing attention due to the merit of a low
attack cost via changing a small number of pixels. However, the generated adversarial …

Studies have shown that deep neural networks (DNNs) are vulnerable to adversarial attack.
Minor malicious modifications of examples will lead to the DNN misclassification. Such …

With its growing use in safety/security-critical applications, Deep Learning (DL) has raised
increasing concerns regarding its dependability. In particular, DL has a notorious problem of …

State-of-the-art deep neural networks in image classification, recognition, and detection
tasks are increasingly being used in a range of real-world applications. Applications include …

Adversarial sample generation problem is a hot issue in the security field of deep learning.
Evolutionary algorithm has been widely used to solve this problem in recent years because …

Adversarial patch attacks are commonly used to attack aerial imagery object detectors.
However, most existing methods are designed for white-box settings, which are impractical …

Physical adversarial examples (AEs) have become an increasing threat to deploying deep
neural network (DNN) models in the real world. Popular approaches adopt sticking-based or …

Some evolutionary algorithms have been proposed to address biparty multiobjective
optimization problems (BPMOPs). However, all these algorithms are centralized algorithms …