Results 41 to 50 of about 6,341 (246)

Adversarial attacks against supervised machine learning based network intrusion detection systems.

PLoS ONE, 2022
Adversarial machine learning is a recent area of study that explores both adversarial attack strategy and detection systems of adversarial attacks, which are inputs specially crafted to outwit the classification of detection systems or disrupt the ...
Ebtihaj Alshahrani, Daniyal Alghazzawi, Reem Alotaibi, Osama Rabie   +3 more
doaj   +2 more sources

Using Frequency Attention to Make Adversarial Patch Powerful Against Person Detector

IEEE Access, 2023
Deep neural networks (DNNs) are vulnerable to adversarial attacks. In particular, object detectors may be attacked by applying a particular adversarial patch to the image.
Xiaochun Lei, Xiang Cai, Chang Lu, Zetao Jiang, Zhaoting Gong, Linjun Lu   +5 more
doaj   +1 more source

Real-Time Adversarial Attacks [PDF]

Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, 2019
In recent years, many efforts have demonstrated that modern machine learning algorithms are vulnerable to adversarial attacks, where small, but carefully crafted, perturbations on the input can make them fail. While these attack methods are very effective, they only focus on scenarios where the target model takes static input, i.e., an attacker can ...
Yuan Gong 0001, Boyang Li 0003, Christian Poellabauer, Yiyu Shi 0001   +3 more
openaire   +2 more sources

Multi-Targeted Adversarial Example in Evasion Attack on Deep Neural Network

IEEE Access, 2018
Deep neural networks (DNNs) are widely used for image recognition, speech recognition, pattern analysis, and intrusion detection. Recently, the adversarial example attack, in which the input data are only slightly modified, although not an issue for ...
Hyun Kwon, Yongchul Kim, Ki-Woong PARK, Hyunsoo Yoon, Daeseon Choi   +4 more
doaj   +1 more source

Multi-Stage Adversarial Defense for Online DDoS Attack Detection System in IoT

IEEE Access
Machine learning-based Distributed Denial of Service (DDoS) attack detection systems have proven effective in detecting and preventing DDoD attacks in Internet of Things (IoT) systems.
Yonas Kibret Beshah, Surafel Lemma Abebe, Henock Mulugeta Melaku   +2 more
doaj   +1 more source

Care and COVID 19: Lessons for liberals and neoliberals

Child &Family Social Work, EarlyView., 2023
Abstract Within the liberal political traditions, care is regarded as a private matter, a problem of ethics rather than justice. Social justice is framed as an issue of economics (re/distribution), culture (recognition) and/or politics (representation).
Kathleen Lynch
wiley   +1 more source

Adversarial Attacks on Human Vision

CoRR, 2022
This article presents an introduction to visual attention retargeting, its connection to visual saliency, the challenges associated with it, and ideas for how it can be approached. The difficulty of attention retargeting as a saliency inversion problem lies in the lack of one-to-one mapping between saliency and the image domain, in addition to the ...
Victor A. Mateescu, Ivan V. Bajic
openaire   +2 more sources

StackingNet: Collective Inference Across Independent AI Foundation Models

Advanced Science, EarlyView.
ABSTRACT Artificial intelligence (AI) built on large foundation models has transformed language understanding, computer vision, and reasoning, yet these systems remain isolated and cannot readily share their capabilities. Coordinating the complementary strengths of independently developed, black‐box foundation models is essential for trustworthy ...
Siyang Li, Chenhao Liu, Dongrui Wu, Zhigang Zeng, Lieyun Ding   +4 more
wiley   +1 more source

Time Resolved DNA Barcodes for Information Encoding and Dynamic Encryption

Advanced Science, EarlyView.
This study establishes a molecular information platform based on DNA Temporal Barcodes. Information is encoded through combinations of DNA tags with distinct retention times, while dynamic encryption is achieved through a key‐triggered DNA ligation.
Likang Chu, Haixia Wang, Haiyan Gao, Sixian Chen, Zilong Li, Zhanyihao Hao, Lei He, Da Han   +7 more
wiley   +1 more source

Comprehensive comparisons of gradient-based multi-label adversarial attacks

Complex & Intelligent Systems
Adversarial examples which mislead deep neural networks by adding well-crafted perturbations have become a major threat to classification models. Gradient-based white-box attack algorithms have been widely used to generate adversarial examples.
Zhijian Chen, Wenjian Luo, Muhammad Luqman Naseem, Linghao Kong, Xiangkai Yang   +4 more
doaj   +1 more source