Deep learning methods can not only detect false data injection attacks (FDIA) but also locate
attacks of FDIA. Although adversarial false data injection attacks (AFDIA) based on deep …

Many works have shown that the adversarial examples being generated on a known
substitute model have the ability to mislead other unknown black-box models, which has …

Adversarial attacks on point clouds are crucial for assessing and improving the adversarial
robustness of 3D deep learning models. Despite leveraging various geometric constraints …

Many black-box adversarial attack algorithms perform attacks on machine learning models
based on the transferability of adversarial examples, and the input transformation-based …

Graph Neural Network (GNN)-based fake news detectors apply various methods to construct
graphs, aiming to learn distinctive news embeddings for classification. Since the …

Robust deep learning models have demonstrated significant applicability in real-world
scenarios. The utilization of adversarial attacks plays a crucial role in assessing the …

Exploring adversarial attacks on deep neural networks (DNNs) is crucial for assessing and
enhancing their adversarial robustness. Among various attack types, hard-label attacks that …

Adversarial attacks on 3D point clouds often exhibit unsatisfactory imperceptibility, which
primarily stems from the disregard for manifold-aware distortion, ie, distortion of the …

Face recognition systems based on deep learning have recently demonstrated an
outstanding success in solving complex issues. Yet they turn out to be very vulnerable to …

The advent of the big data era has brought unprecedented data demands. The integration of
computing resources with network resources in the computing force network enables the …