Results 31 to 40 of about 804,777 (293)
Demotivate Adversarial Defense in Remote Sensing [PDF]
2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, 2021 Convolutional neural networks are currently the state-of-the-art algorithms for many remote sensing applications such as semantic segmentation or object detection. However, these algorithms are extremely sensitive to over-fitting, domain change and adversarial examples specifically designed to fool them.
Adrien Chan-Hon-Tong +2 more
openaire +3 more sources
Open-Set Adversarial Defense [PDF]
, 2020 Accepted by ECCV ...
Rui Shao 0001 +3 more
openaire +3 more sources
Attacking Adversarial Attacks as A Defense
CoRR, 2021 It is well known that adversarial attacks can fool deep neural networks with imperceptible perturbations. Although adversarial training significantly improves model robustness, failure cases of defense still broadly exist. In this work, we find that the adversarial attacks can also be vulnerable to small perturbations.
Boxi Wu +8 more
openaire +3 more sources
Understanding and Improving Ensemble Adversarial Defense [PDF]
, 2023 The strategy of ensemble has become popular in adversarial defense, which trains multiple base classifiers to defend against adversarial attacks in a cooperative manner.
Mu, Tingting, Deng, Yian
core +3 more sources
Adversarial Ranking Attack and Defense [PDF]
, 2020 Deep Neural Network (DNN) classifiers are vulnerable to adversarial attack, where an imperceptible perturbation could result in misclassification. However, the vulnerability of DNN-based image ranking systems remains under-explored. In this paper, we propose two attacks against deep ranking systems, i.e., Candidate Attack and Query Attack, that can ...
Mo Zhou +4 more
openaire +3 more sources
ATWM: Defense against adversarial malware based on adversarial training [PDF]
, 2023 Deep learning technology has made great achievements in the field of image. In order to defend against malware attacks, researchers have proposed many Windows malware detection models based on deep learning.
Li, Kun, Guo, Wei, Zhang, Fan
core +1 more source
Adversarial Defenses via a Mixture of Generators [PDF]
, 2021 In spite of the enormous success of neural networks, adversarial examples remain a relatively weakly understood feature of deep learning systems. There is a considerable effort in both building more powerful adversarial attacks and designing methods to counter the effects of adversarial examples.
Maciej Zelaszczyk, Jacek Mandziuk
openaire +4 more sources
Survey on adversarial attacks and defense of face forgery and detection
网络与信息安全学报, 2023 Face forgery and detection has become a research hotspot.Face forgery methods can produce fake face images and videos.Some malicious videos, often targeting celebrities, are widely circulated on social networks, damaging the reputation of victims and ...
Shiyu HUANG, Feng YE, Tianqiang HUANG, Wei LI, Liqing HUANG, Haifeng LUO
doaj +3 more sources
Robust Rumor Detection based on Multi-Defense Model Ensemble
Applied Artificial Intelligence, 2023 The development of adversarial technology, represented by adversarial text, has brought new challenges to rumor detection based on deep learning. In order to improve the robustness of rumor detection models under adversarial conditions, we propose a ...
Fan Yang, Shaomei Li
doaj +1 more source
Adversarial Attacks Defense Method Based on Multiple Filtering and Image Rotation
Discrete Dynamics in Nature and Society, 2022 Adversarial examples in an image classification task cause neural networks to predict incorrect class labels with high confidence. Many applications related to image classification, such as self-driving and facial recognition, have been seriously ...
Feng Li, Xuehui Du, Liu Zhang
doaj +1 more source