Results 41 to 50 of about 96,849 (322)

Secure machine learning against adversarial samples at test time

EURASIP Journal on Information Security, 2022
Deep neural networks (DNNs) are widely used to handle many difficult tasks, such as image classification and malware detection, and achieve outstanding performance.
Jing Lin, Laurent L. Njilla, Kaiqi Xiong
doaj   +1 more source

Impact of adversarial examples on deep learning models for biomedical image segmentation [PDF]

, 2019
Deep learning models, which are increasingly being used in the field of medical image analysis, come with a major security risk, namely, their vulnerability to adversarial examples.
C Pena-Betancor, M Everingham, O Ronneberger, T Heimann   +3 more
core   +4 more sources

Query complexity of adversarial attacks

CoRR, 2020
There are two main attack models considered in the adversarial robustness literature: black-box and white-box. We consider these threat models as two ends of a fine-grained spectrum, indexed by the number of queries the adversary can ask. Using this point of view we investigate how many queries the adversary needs to make to design an attack that is ...
Grzegorz Gluch, Rüdiger L. Urbanke
openaire   +3 more sources

Adversarial Attacks and Defenses

ACM SIGKDD Explorations Newsletter, 2021
Despite the recent advances in a wide spectrum of applications, machine learning models, especially deep neural networks, have been shown to be vulnerable to adversarial attacks. Attackers add carefully-crafted perturbations to input, where the perturbations are almost imperceptible to humans, but can cause models to make wrong predictions.
Ninghao Liu, Mengnan Du, Ruocheng Guo, Huan Liu 0001, Xia Hu 0001   +4 more
openaire   +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

HopSkipJumpAttack: A Query-Efficient Decision-Based Attack

, 2020
The goal of a decision-based adversarial attack on a trained model is to generate adversarial examples based solely on observing output labels returned by the targeted model.
Chen, Jianbo, Jordan, Michael I., Wainwright, Martin J.   +2 more
core   +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

Robust Audio Adversarial Example for a Physical Attack

, 2019
We propose a method to generate audio adversarial examples that can attack a state-of-the-art speech recognition model in the physical world. Previous work assumes that generated adversarial examples are directly fed to the recognition model, and is not ...
Sakuma, Jun, Yakura, Hiromu
core   +1 more source

Using LIP to Gloss Over Faces in Single-Stage Face Detection Networks

, 2018
This work shows that it is possible to fool/attack recent state-of-the-art face detectors which are based on the single-stage networks. Successfully attacking face detectors could be a serious malware vulnerability when deploying a smart surveillance ...
D Chen, K Zhang, T Yamada, T-Y Lin, W Liu, Y Li   +5 more
core   +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