Results 51 to 60 of about 156,834 (158)

How to beat a Bayesian adversary

European Journal of Applied Mathematics
Deep neural networks and other modern machine learning models are often susceptible to adversarial attacks. Indeed, an adversary may often be able to change a model’s prediction through a small, directed perturbation of the model’s input – an issue in ...
Zihan Ding, Kexin Jin, Jonas Latz, Chenguang Liu   +3 more
doaj   +1 more source

Machine learning security and privacy:a survey

网络与信息安全学报, 2018
As an important method to implement artificial intelligence,machine learning technology is widely used in data mining,computer vision,natural language processing and other fields.With the development of machine learning,it brings amount of security and ...
Lei SONG,Chunguang MA,Guanghan DUAN
doaj   +3 more sources

Multitask adversarial attack with dispersion amplification

EURASIP Journal on Information Security, 2021
Recently, adversarial attacks have drawn the community’s attention as an effective tool to degrade the accuracy of neural networks. However, their actual usage in the world is limited.
Pavlo Haleta, Dmytro Likhomanov, Oleksandra Sokol   +2 more
doaj   +1 more source

Bilevel Models for Adversarial Learning and a Case Study

Mathematics
Adversarial learning has been attracting more and more attention thanks to the fast development of machine learning and artificial intelligence. However, due to the complicated structure of most machine learning models, the mechanism of adversarial ...
Yutong Zheng, Qingna Li
doaj   +1 more source

Classical autoencoder distillation of quantum adversarial manipulations

Physical Review Research
Quantum neural networks have been proven robust against classical adversarial attacks, but their vulnerability against quantum adversarial attacks is still a challenging problem.
Amena Khatun, Muhammad Usman
doaj   +1 more source

Adversarial Challenges in Network Intrusion Detection Systems: Research Insights and Future Prospects

IEEE Access
Machine learning has brought significant advances in cybersecurity, particularly in the development of Intrusion Detection Systems (IDS). These improvements are mainly attributed to the ability of machine learning algorithms to identify complex ...
Sabrine Ennaji, Fabio de Gaspari, Dorjan Hitaj, Alicia Kbidi, Luigi Vincenzo Mancini   +4 more
doaj   +1 more source

Learning atomic forces from uncertainty-calibrated adversarial attacks

npj Computational Materials
Adversarial approaches, which intentionally challenge machine learning models by generating difficult examples, are increasingly being adopted to improve machine learning interatomic potentials (MLIPs).
Henrique Musseli Cezar, Tilmann Bodenstein, Henrik Andersen Sveinsson, Morten Ledum, Simen Reine, Sigbjørn Løland Bore   +5 more
doaj   +1 more source

Adversarial Halftone QR Code

IEEE Access
Recent studies have shown that machine-learning models are vulnerable to adversarial attacks. Adversarial attacks are deliberate attempts to modify the input data of a machine learning model in a way that causes it to produce incorrect predictions.
Palakorn Kamnounsing, Karin Sumongkayothin, Prarinya Siritanawan, Kazunori Kotani   +3 more
doaj   +1 more source

Law and Adversarial Machine Learning

, 2018
When machine learning systems fail because of adversarial manipulation, how should society expect the law to respond? Through scenarios grounded in adversarial ML literature, we explore how some aspects of computer crime, copyright, and tort law interface with perturbation, poisoning, model stealing and model inversion attacks to show how some attacks ...
Kumar, Ram Shankar Siva, O'Brien, David R., Albert, Kendra, Vilojen, Salome   +3 more
openaire   +2 more sources

Adversarial Attacks and Defenses in Fault Detection and Diagnosis: A Comprehensive Benchmark on the Tennessee Eastman Process

IEEE Open Journal of the Industrial Electronics Society
Integrating machine learning into Automated Control Systems (ACS) enhances decision-making in industrial process management. One of the limitations to the widespread adoption of these technologies in industry is the vulnerability of neural networks to ...
Vitaliy Pozdnyakov, Aleksandr Kovalenko, Ilya Makarov, Mikhail Drobyshevskiy, Kirill Lukyanov   +4 more
doaj   +1 more source