Efficient black-box attack with surrogate models and multiple universal adversarial perturbations [PDF]
Scientific ReportsDeep learning models are inherently vulnerable to adversarial examples, particularly in black-box settings where attackers have limited knowledge of the target model.
Tao Ma +4 more
doaj +2 more sources
An Optimized Black-Box Adversarial Simulator Attack Based on Meta-Learning
Entropy, 2022 Much research on adversarial attacks has proved that deep neural networks have certain security vulnerabilities. Among potential attacks, black-box adversarial attacks are considered the most realistic based on the the natural hidden nature of deep ...
Zhiyu Chen +7 more
doaj +3 more sources
ABCAttack: A Gradient-Free Optimization Black-Box Attack for Fooling Deep Image Classifiers [PDF]
Entropy, 2022 Prosanta Gope +2 more
exaly +2 more sources
Adversarial Example Generation Method Based on Improved Genetic Algorithm [PDF]
Jisuanji gongcheng, 2023 The adversarial example is an effective tool to evaluate the security and robustness of a model.Conducting antagonism training on the model can effectively improve the model's security.The mainstream classification methods divide the existing ...
BAI Zhixu, WANG Hengjun
doaj +1 more source
Black Box Adversarial Attack Starting Point Promotion Method Based on Mobility Between Models [PDF]
Jisuanji gongcheng, 2021 In order to efficiently find the adversarial samples under the decision-based black box attacks, a method using the mobility between models is proposed to enhance the adversarial starting point. The mobility is used to circularly superimpose interference
CHEN Xiaonan, HU Jianmin, ZHANG Benjun, CHEN Ailing
doaj +1 more source
Investigating vulnerabilities of gait recognition model using latent-based perturbations [PDF]
Scientific ReportsVideo surveillance systems are very beneficial in strengthening security and tracking events and activities in a variety of contexts, including public venues.
Zeeshan Ali +5 more
doaj +2 more sources
Besting the Black-Box: Barrier Zones for Adversarial Example Defense
IEEE Access, 2022 Adversarial machine learning defenses have primarily been focused on mitigating static, white-box attacks. However, it remains an open question whether such defenses are robust under an adaptive black-box adversary.
Kaleel Mahmood +4 more
doaj +1 more source
Comparative Research on Application of Adversarial Samples for End-to-End Speaker Identification [PDF]
Jisuanji gongcheng, 2021 In order to explore the security threats and attack effects of the adversarial samples on the end-to-end speaker identification system, this paper analyzes five white box algorithms(FGSM, JSMA, BIM, C&W, PGD) and two black box algorithms(ZOO, HSJA ...
LIAO Junfan, GU Yijun, ZHANG Peijing, LIAO Qian
doaj +1 more source
Adversarial Examples Generation Method Based on Random Translation Transformation [PDF]
Jisuanji gongcheng, 2022 The image classification model based on Deep Neural Network(DNN) can recognize images with a recognition degree that is even higher than that of human eyes.However, it is vulnerable to attacks from adversarial examples because of the fragility of the ...
LI Zheming, ZHANG Hengwei, MA Junqiang, WANG Jindong, YANG Bo
doaj +1 more source
Algorithms, 2022 Deep neural networks (DNNs) are sensitive to adversarial data in a variety of scenarios, including the black-box scenario, where the attacker is only allowed to query the trained model and receive an output.
Raz Lapid, Zvika Haramaty, Moshe Sipper
doaj +1 more source