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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 adversarial robustness in computational pathology

Nature Communications, 2022
AbstractArtificial Intelligence (AI) can support diagnostic workflows in oncology by aiding diagnosis and providing biomarkers directly from routine pathology slides. However, AI applications are vulnerable to adversarial attacks. Hence, it is essential to quantify and mitigate this risk before widespread clinical use.
Narmin Ghaffari Laleh, Daniel Truhn, Gregory Patrick Veldhuizen, Tianyu Han, Marko van Treeck, Roman D. Buelow, Rupert Langer, Bastian Dislich, Peter Boor, Volkmar Schulz, Jakob Nikolas Kather   +10 more
openaire   +4 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 0001, Mengnan Du, Ruocheng Guo, Huan Liu 0001, Xia Hu 0001   +4 more
openaire   +2 more sources

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

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

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
doaj   +1 more source

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

Launching Adversarial Attacks against Network Intrusion Detection Systems for IoT

, 2021
As the internet continues to be populated with new devices and emerging technologies, the attack surface grows exponentially. Technology is shifting towards a profit-driven Internet of Things market where security is an afterthought.
William J. Buchanan, Buchanan, William J., Alexios Mylonas, Mylonas, Alexios, Pavlos Papadopoulos, Pitropakis, Nikolaos, Thornewill Von Essen, Oliver, Papadopoulos, Pavlos, Chrysoulas, Christos, Essen, Oliver Thornewill von, Oliver Thornewill von Essen, Nikolaos Pitropakis, Christos Chrysoulas, Chrysoulas, Christos; id_orcid   +13 more
core   +2 more sources

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

Wasserstein Adversarial Robustness [PDF]

, 2020
Deep models, while being extremely flexible and accurate, are surprisingly vulnerable to ``small, imperceptible'' perturbations known as adversarial attacks.
Wu, Kaiwen
core