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Adversarial Examples in the Physical World [PDF]

, 2018
Most existing machine learning classifiers are highly vulnerable to adversarial examples. An adversarial example is a sample of input data which has been modified very slightly in a way that is intended to cause a machine learning classifier to misclassify it.
Alexey Kurakin, Ian J. Goodfellow, Samy Bengio   +2 more
openaire   +3 more sources

A Framework for Robust Deep Learning Models Against Adversarial Attacks Based on a Protection Layer Approach

IEEE Access
Deep learning (DL) has demonstrated remarkable achievements in various fields. Nevertheless, DL models encounter significant challenges in detecting and defending against adversarial samples (AEs).
Mohammed Nasser Al-Andoli, Shing Chiang Tan, Kok Swee Sim, Pey Yun Goh, Chee Peng Lim   +4 more
doaj   +1 more source

Detecting Adversarial Examples

CoRR
Deep Neural Networks (DNNs) have been shown to be vulnerable to adversarial examples. While numerous successful adversarial attacks have been proposed, defenses against these attacks remain relatively understudied. Existing defense approaches either focus on negating the effects of perturbations caused by the attacks to restore the DNNs' original ...
Furkan Mumcu, Yasin Yilmaz 0001
openaire   +2 more sources

Understanding Adversarial Robustness Against On-manifold Adversarial Examples

, 2022
Deep neural networks (DNNs) are shown to be vulnerable to adversarial examples. A well-trained model can be easily attacked by adding small perturbations to the original data.
Luo, Zhi-Quan, Yang, Liusha, Wang, Jue, Fan, Yanbo, Xiao, Jiancong   +4 more
core  

Theoretical Robustness Bounds on the Successful Adversarial Examples in Probabilistic Models: Comprehensive Insights From Gaussian Processes

IEEE Access
An adversarial example (AE) is an attack method targeting machine learning, which is crafted by adding an imperceptible perturbation to input data to induce misclassification.
Hiroaki Maeshima, Akira Otsuka
doaj   +1 more source

A Brute-Force Black-Box Method to Attack Machine Learning-Based Systems in Cybersecurity

IEEE Access, 2020
Machine learning algorithms are widely utilized in cybersecurity. However, recent studies show that machine learning algorithms are vulnerable to adversarial examples.
Sicong Zhang, Xiaoyao Xie, Yang Xu
doaj   +1 more source

Analysis of Adversarial Examples [PDF]

The rise of artificial intelligence (AI) has significantly impacted the field of computer vision (CV). In particular, deep learning (DL) has advanced the development of algorithms that comprehend visual data.
Lorenz, Peter
core   +1 more source

Adversarial Examples in Physical World [PDF]

Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, 2021
Although deep neural networks (DNNs) have already made fairly high achievements and a very wide range of impact, their vulnerability attracts lots of interest of researchers towards related studies about artificial intelligence (AI) safety and robustness this year.
openaire   +1 more source

A Multi‐Scale Machine Learning Framework for the Inverse Design of High Entropy Alloys

Advanced Engineering Materials, EarlyView.
High‐entropy alloys offer vast potential for various applications, including electrocatalysis; however, their compositional complexity challenges conventional screening. We introduce an inverse‐design framework combining two neural networks to determine optimal compositions and reconstruct nanoparticle geometry from targeted properties and conventional
Mikael Takoutsin, Marta Campolucci, Nicolas de Andrade Ishiki, Nathan Mirgot, Nadir Zhamantay, Frédéric Kanoufi, Jennifer Peron, Jean Charlety, Emiliano Fonda, Valérie Briois, Marco Faustini, Maria‐Letizia De Marco, Christine Goyhenex, Ovidiu Ersen, Hervé Bulou   +14 more
wiley   +1 more source

On the Effect of Adversarial Training Against Invariance-based Adversarial Examples [PDF]

, 2023
Adversarial examples are carefully crafted attack points that are supposed to fool machine learning classifiers. In the last years, the field of adversarial machine learning, especially the study of perturbation-based adversarial examples, in which a ...
Merkle, Florian, Nocker, Martin, Schöttle, Pascal, Rauter, Roland   +3 more
core   +1 more source