Results 61 to 70 of about 1,236,597 (154)
Discrete Adversarial Attack to Models of Code
Proc. ACM Program. Lang., 2023 The pervasive brittleness of deep neural networks has attracted significant attention in recent years. A particularly interesting finding is the existence of adversarial examples, imperceptibly perturbed natural inputs that induce erroneous predictions ...
Fengjuan Gao, Yu Wang, Ke Wang
semanticscholar +1 more source
IoU Attack: Towards Temporally Coherent Black-Box Adversarial Attack for Visual Object Tracking [PDF]
Computer Vision and Pattern Recognition, 2021 Adversarial attack arises due to the vulnerability of deep neural networks to perceive input samples injected with imperceptible perturbations. Recently, adversarial attack has been applied to visual object tracking to evaluate the robustness of deep ...
Shuai Jia +3 more
semanticscholar +1 more source
Survey of Adversarial Attacks and Defense Methods for Deep Learning Model [PDF]
Jisuanji gongcheng, 2021 As an important part of artificial intelligence technology,deep learning is widely used in computer vision,natural language processing and other fields.Although deep learning performs well in tasks such as image classification and target detection,its ...
JIANG Yan, ZHANG Liguo
doaj +1 more source
FCA: Learning a 3D Full-coverage Vehicle Camouflage for Multi-view Physical Adversarial Attack [PDF]
AAAI Conference on Artificial Intelligence, 2021 Physical adversarial attacks in object detection have attracted increasing attention. However, most previous works focus on hiding the objects from the detector by generating an individual adversarial patch, which only covers the planar part of the ...
Donghua Wang +7 more
semanticscholar +1 more source
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
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
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
Adversarial attacks and adversarial robustness in computational pathology
Nature Communications, 2022 AbstractArtificial Intelligence (AI) can support diagnostic workflows in oncology by aiding diagnosis and providing biomarkers directly from routine pathology slides. However, AI applications are vulnerable to adversarial attacks. Hence, it is essential to quantify and mitigate this risk before widespread clinical use.
Narmin Ghaffari Laleh +10 more
openaire +4 more sources
Adversarial Attacks and Defenses
ACM SIGKDD Explorations Newsletter, 2021 Despite the recent advances in a wide spectrum of applications, machine learning models, especially deep neural networks, have been shown to be vulnerable to adversarial attacks. Attackers add carefully-crafted perturbations to input, where the perturbations are almost imperceptible to humans, but can cause models to make wrong predictions.
Ninghao Liu 0001 +4 more
openaire +2 more sources
Secure machine learning against adversarial samples at test time
EURASIP Journal on Information Security, 2022 Deep neural networks (DNNs) are widely used to handle many difficult tasks, such as image classification and malware detection, and achieve outstanding performance.
Jing Lin, Laurent L. Njilla, Kaiqi Xiong
doaj +1 more source