Results 31 to 40 of about 85,609 (269)
Probabilistic Categorical Adversarial Attack & Adversarial Training
CoRR, 2022 The existence of adversarial examples brings huge concern for people to apply Deep Neural Networks (DNNs) in safety-critical tasks. However, how to generate adversarial examples with categorical data is an important problem but lack of extensive exploration.
Xu, Han +6 more
openaire +2 more sources
Functional Adversarial Attacks
CoRR, 2019 Accepted to NeurIPS ...
Cassidy Laidlaw, Soheil Feizi
openaire +3 more sources
Adversarial Attack Attribution: Discovering Attributable Signals in Adversarial ML Attacks
CoRR, 2021 Accepted to RSEML Workshop at AAAI ...
Marissa Dotter +5 more
openaire +2 more sources
Robustness of Deep Learning Models for Vision Tasks
Applied Sciences, 2023 In recent years, artificial intelligence technologies in vision tasks have gradually begun to be applied to the physical world, proving they are vulnerable to adversarial attacks.
Youngseok Lee, Jongweon Kim
doaj +1 more source
Exploring Adversarial Robustness of LiDAR Semantic Segmentation in Autonomous Driving
Sensors, 2023 Deep learning networks have demonstrated outstanding performance in 2D and 3D vision tasks. However, recent research demonstrated that these networks result in failures when imperceptible perturbations are added to the input known as adversarial attacks.
K. T. Yasas Mahima +3 more
doaj +1 more source
Distributionally Adversarial Attack
Proceedings of the AAAI Conference on Artificial Intelligence, 2019 Recent work on adversarial attack has shown that Projected Gradient Descent (PGD) Adversary is a universal first-order adversary, and the classifier adversarially trained by PGD is robust against a wide range of first-order attacks. It is worth noting that the original objective of an attack/defense model relies on a data distribution p(x), typically ...
Tianhang Zheng +2 more
openaire +3 more sources
Query complexity of adversarial attacks
CoRR, 2020 There are two main attack models considered in the adversarial robustness literature: black-box and white-box. We consider these threat models as two ends of a fine-grained spectrum, indexed by the number of queries the adversary can ask. Using this point of view we investigate how many queries the adversary needs to make to design an attack that is ...
Grzegorz Gluch, Rüdiger L. Urbanke
openaire +3 more sources
Boosting 3D Adversarial Attacks With Attacking on Frequency
IEEE Access, 2022 Deep neural networks (DNNs) have been shown to be vulnerable to adversarial attacks in the image domain. Recently, 3D adversarial attacks, especially adversarial attacks on point clouds, have elicited mounting interest.
Binbin Liu, Jinlai Zhang, Jihong Zhu
doaj +1 more source
Gotta Catch 'Em All: Using Honeypots to Catch Adversarial Attacks on Neural Networks
, 2020 Deep neural networks (DNN) are known to be vulnerable to adversarial attacks. Numerous efforts either try to patch weaknesses in trained models, or try to make it difficult or costly to compute adversarial examples that exploit them.
Li, Bo +5 more
core +1 more source
Exploring Diverse Feature Extractions for Adversarial Audio Detection
IEEE Access, 2023 Although deep learning models have exhibited excellent performance in various domains, recent studies have discovered that they are highly vulnerable to adversarial attacks.
Yujin Choi +3 more
doaj +1 more source