Results 51 to 60 of about 1,143,792 (206)

A Systematic Review of Adversarial Machine Learning and Deep Learning Applications

Al-Iraqia Journal for Scientific Engineering Research
The review delves into creating an understandable framework for machine learning in robotics. It stresses the significance of machine learning in materials science and robotics highlighting how it can transform industries by boosting efficiency and ...
Tabarak Ali Abdalkareem, Khamis A. Zidan, A. S. Albahri   +2 more
doaj   +1 more source

Pemanfaatan Deep Convolutional Auto-encoder untuk Mitigasi Serangan Adversarial Attack pada Citra Digital

J-Intech (Journal of Information and Technology), 2023
Serangan adversarial pada citra digital merupakan ancaman serius bagi penggunaan teknologi machine learning dalam berbagai aplikasi kehidupan sehari-hari. Teknik Fast Gradient Sign Method (FGSM) telah terbukti efektif dalam melakukan serangan pada model
Putu Widiarsa Kurniawan S, Yosi Kristian, Joan Santoso   +2 more
doaj   +1 more source

SURVEY AND PROPOSED METHOD TO DETECT ADVERSARIAL EXAMPLES USING AN ADVERSARIAL RETRAINING MODEL

Tạp chí Khoa học Đại học Đà Lạt
Artificial intelligence (AI) has found applications across various sectors and industries, offering numerous advantages to human beings. One prominent area where AI has made significant contributions is in machine learning models.
Thanh Son Phan, Quang Hua Ta, Duy Trung Pham, Phi Ho Truong   +3 more
doaj   +1 more source

Enhancing adversarial robustness of quantum neural networks by adding noise layers

New Journal of Physics, 2023
The rapid advancements in machine learning and quantum computing have given rise to a new research frontier: quantum machine learning. Quantum models designed for tackling classification problems possess the potential to deliver speed enhancements and ...
Chenyi Huang, Shibin Zhang
doaj   +1 more source

AutoBayes: Automated Bayesian Graph Exploration for Nuisance- Robust Inference

IEEE Access, 2021
Learning data representations that capture task-related features, but are invariant to nuisance variations remains a key challenge in machine learning.
Andac Demir, Toshiaki Koike-Akino, Ye Wang, Deniz Erdogmus   +3 more
doaj   +1 more source

A survey of practical adversarial example attacks

Cybersecurity, 2018
Adversarial examples revealed the weakness of machine learning techniques in terms of robustness, which moreover inspired adversaries to make use of the weakness to attack systems employing machine learning.
Lu Sun, Mingtian Tan, Zhe Zhou
doaj   +1 more source

Universal Adversarial Training Using Auxiliary Conditional Generative Model-Based Adversarial Attack Generation

Applied Sciences, 2023
While Machine Learning has become the holy grail of modern-day computing, it has many security flaws that have yet to be addressed and resolved. Adversarial attacks are one of these security flaws, in which an attacker appends noise to data samples that ...
Hiskias Dingeto, Juntae Kim
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

Attack-agnostic Adversarial Detection on Medical Data Using Explainable\n Machine Learning [PDF]

, 2021
Matthew Watson, Noura Al Moubayed
openalex   +1 more source

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