Results 101 to 110 of about 505,564 (200)

AdvHash: Set-to-set Targeted Attack on Deep Hashing with One Single Adversarial Patch

, 2021
In this paper, we propose AdvHash, the first targeted mismatch attack on deep hashing through adversarial patch. After superimposed with the same adversarial patch, any query image with a chosen label will retrieve a set of irrelevant images with the ...
Hai Jin, Liu, X, Xiaogeng Liu, Minghui Li, Li, M, Jin, H, Zhang, LY, Shengshan Hu, Zhang, Y, Yechao Zhang, Leo Yu Zhang, Hu, S   +11 more
core   +1 more source

Physically structured adversarial patch inspired by natural leaves multiply angles deceives infrared detectors

Journal of King Saud University: Computer and Information Sciences
Researching infrared adversarial attacks is crucial for ensuring the safe deployment of security-sensitive systems reliant on infrared object detectors.
Zhiyang Hu, Xing Yang, Jiwen Zhao, Haoqi Gao, Haoli Xu, Hua Mu, Yangyang Wang   +6 more
doaj   +1 more source

DIPA: Adversarial Attack on DNNs by Dropping Information and Pixel-Level Attack on Attention

Information
Deep neural networks (DNNs) have shown remarkable performance across a wide range of fields, including image recognition, natural language processing, and speech processing. However, recent studies indicate that DNNs are highly vulnerable to well-crafted
Jing Liu, Huailin Liu, Pengju Wang, Yang Wu, Keqin Li   +4 more
doaj   +1 more source

Adversarial Attacks and Defences: A Survey

CoRR, 2018
Deep learning has emerged as a strong and efficient framework that can be applied to a broad spectrum of complex learning problems which were difficult to solve using the traditional machine learning techniques in the past. In the last few years, deep learning has advanced radically in such a way that it can surpass human-level performance on a number ...
Anirban Chakraborty 0003, Manaar Alam, Vishal Dey, Anupam Chattopadhyay, Debdeep Mukhopadhyay   +4 more
openaire   +2 more sources

Black-Box Universal Adversarial Attack for DNN-Based Models of SAR Automatic Target Recognition

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Synthetic aperture radar automatic target recognition (SAR-ATR) models based on deep neural networks (DNNs) are vulnerable to attacks of adversarial examples. Universal adversarial attack algorithms can help evaluate and improve the robustness of the SAR-
Xuanshen Wan, Wei Liu, Chaoyang Niu, Wanjie Lu, Meng Du, Yuanli Li   +5 more
doaj   +1 more source

Adversarial Attacks on Hyperbolic Networks

As hyperbolic deep learning grows in popularity, so does the need for adversarial robustness in the context of such a non-Euclidean geometry. To this end, this paper proposes hyperbolic alternatives to the commonly used FGM and PGD adversarial attacks.
Max van Spengler, Jan Zahálka, Pascal Mettes   +2 more
openaire   +4 more sources

ASTrA: Adversarial Self-supervised Training with Adaptive-Attacks

Existing self-supervised adversarial training (self-AT) methods rely on hand-crafted adversarial attack strategies for PGD attacks, which fail to adapt to the evolving learning dynamics of the model and do not account for instance-specific ...
Saini, Rajkumar, Shah, Mubarak, Vashishtha, Gautam, Liwicki, Marcus, Chhipa, Prakash Chandra, Anantha sai Settur, Jithamanyu   +5 more
core   +2 more sources

Research on adversarial attack and defense of photovoltaic power prediction

Dianzi Jishu Yingyong
Deep neural networks have been widely used in photovoltaic power prediction, but they are vulnerable to adversarial attacks. In order to improve the robustness of the prediction model, an adversarial attack algorithm based on fast gradient sign method ...
Zhou Wang
doaj   +1 more source

Adversarial Attacks on Stochastic Bandits

CoRR, 2018
We study adversarial attacks that manipulate the reward signals to control the actions chosen by a stochastic multi-armed bandit algorithm. We propose the first attack against two popular bandit algorithms: $ε$-greedy and UCB, \emph{without} knowledge of the mean rewards.
Kwang-Sung Jun, Lihong Li 0001, Yuzhe Ma, Xiaojin Zhu 0001   +3 more
openaire   +4 more sources

PANDA: Practical Adversarial Attack Against Network Intrusion Detection

While adversarial machine learning (AML) attacks have become prevalent in the computer vision (CV) domain, their applications in other domains, such as network intrusion detection systems (NIDS), remain limited.
Kumar, V, Kim, DD, Swain, SK, Bai, G
core   +1 more source