Owing to the extensive application of infrared object detectors in the safety-critical tasks, it is
necessary to evaluate their robustness against adversarial examples in the real world …

Deep neural networks (DNNs) have been shown to be vulnerable to adversarial examples,
which can produce erroneous predictions by injecting imperceptible perturbations. In this …

In adversarial machine learning, deep neural networks can fit the adversarial examples on
the training dataset but have poor generalization ability on the test set. This phenomenon is …

Deep neural networks (DNNs) are susceptible to adversarial images, raising concerns about
their reliability in safety-critical tasks. Sparse adversarial attacks, which limit the number of …

A powerful category of (invisible) data poisoning attacks modify a subset of training
examples by small adversarial perturbations to change the prediction of certain test-time …

The score-based query attacks (SQAs) pose practical threats to deep neural networks by
crafting adversarial perturbations within dozens of queries, only using the model's output …

Adversarial phenomenon has been widely observed in machine learning (ML) systems,
especially in those using deep neural networks, describing that ML systems may produce …

Owing to the extensive application of infrared object detectors in the safety-critical tasks, it is
necessary to evaluate their robustness against adversarial examples in the real world …

Despite the tremendous progress made by deep learning models in image semantic
segmentation, they typically require large annotated examples, and increasing attention is …

Adversarial generative models, such as Generative Adversarial Networks (GANs), are
widely applied for generating various types of data, ie, images, text, and audio. Accordingly …