A Distributed Biased Boundary Attack Method in Black-Box Attack
Applied Sciences, 2021 The adversarial samples threaten the effectiveness of machine learning (ML) models and algorithms in many applications. In particular, black-box attack methods are quite close to actual scenarios.
Fengtao Xiang +3 more
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Black-box Attack Algorithm for SAR-ATR Deep Neural Networks Based on MI-FGSM [PDF]
Leida xuebaoThe field of Synthetic Aperture Radar Automatic Target Recognition (SAR-ATR) lacks effective black-box attack algorithms. Therefore, this research proposes a migration-based black-box attack algorithm by combining the idea of the Momentum Iterative Fast ...
Xuanshen WAN +3 more
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GraphZOOM: subgraph black-box attack against inductive graph neural networks
Complex & Intelligent SystemsInductive Graph Neural Networks (GNNs) have exhibited outstanding predictive performance across applications like social network analysis and protein structure prediction by leveraging graph topology.
Ruixin Tang +4 more
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Partial Retraining Substitute Model for Query-Limited Black-Box Attacks
Applied Sciences, 2020 Black-box attacks against deep neural network (DNN) classifiers are receiving increasing attention because they represent a more practical approach in the real world than white box attacks.
Hosung Park, Gwonsang Ryu, Daeseon Choi
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GenDroid: A query-efficient black-box android adversarial attack framework
, 2023 The security problems of Android applications have been gradually exposed with the increasing popularity of the Android OS. Machine learning (ML) and deep learning (DL) based Android malware detection is still suffering from adversarial attacks, although
Hongfei Shao +17 more
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Locally Black-box Adversarial Attack on Time Series [PDF]
Jisuanji kexue, 2022 Deep neural networks(DNNs) for time series classification have potential security concerns due to their vulnerability to adversarial attacks.The existing attack methods on time series performglobal perturbation based on gradient information,and the ...
YANG Wen-bo, YUAN Ji-dong
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Key Attack Strategies Against Black-Box DNNs
, 2022 International audienceIn this paper, we examined to what extent and under what settings the confidentiality and integrity of black-box DNNs—which are the most challenging setup of DNNs—can be threatened.
Baghdadi, Amer +3 more
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Image classification adversarial attack with improved resizing transformation and ensemble models [PDF]
PeerJ Computer Science, 2023 Convolutional neural networks have achieved great success in computer vision, but incorrect predictions would be output when applying intended perturbations on original input.
Chenwei Li +3 more
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Adversarial Attack for Deep Steganography Based on Surrogate Training and Knowledge Diffusion
Applied Sciences, 2023 Deep steganography (DS), using neural networks to hide one image in another, has performed well in terms of invisibility, embedding capacity, etc. Current steganalysis methods for DS can only detect or remove secret images hidden in natural images and ...
Fangjian Tao +5 more
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Adversarial Attack Transferability Enhancement Algorithm Based on Input Channel Splitting [PDF]
Jisuanji gongcheng, 2023 The Deep Neural Network(DNN) has been widely used in face recognition, automatic driving, and other scenarios;however, it is vulnerable to attacks by adversarial samples.Methods by which adversarial samples are generated can be classified into white-box ...
ZHENG Desheng, CHEN Jixin, ZHOU Jing, KE Wuping, LU Chao, ZHOU Yong, QIU Qian
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