Results 51 to 60 of about 243,531 (318)

Human-Producible Adversarial Examples

CoRR, 2023
Submitted to ICLR ...
David Khachaturov, Yue Gao 0011, Ilia Shumailov, Robert D. Mullins, Ross J. Anderson, Kassem Fawaz   +5 more
openaire   +2 more sources

POSES: Patch Optimization Strategies for Efficiency and Stealthiness Using eXplainable AI

IEEE Access
Adversarial examples, which are carefully crafted inputs designed to deceive deep learning models, create significant challenges in Artificial Intelligence.
Han-Ju Lee, Jin-Seoung Kim, Han-Jin Lee, Seok-Hwan Choi   +3 more
doaj   +1 more source

Adversarial Attacks to Manipulate Target Localization of Object Detector

IEEE Access
Adversarial attack has gradually become an important branch in the field of artificial intelligence security, where the potential threat brought by adversarial example attack is more not to be ignored.
Kai Xu, Xiao Cheng, Ji Qiao, Jia-Teng Li, Kai-Xuan Ji, Jia-Yong Zhong, Peng Tian, Jian-Xun Mi   +7 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

Are Accuracy and Robustness Correlated?

, 2016
Machine learning models are vulnerable to adversarial examples formed by applying small carefully chosen perturbations to inputs that cause unexpected classification errors.
Boult, Terrance E., Günther, Manuel, Rozsa, Andras   +2 more
core   +1 more source

Simple Transparent Adversarial Examples

CoRR, 2021
There has been a rise in the use of Machine Learning as a Service (MLaaS) Vision APIs as they offer multiple services including pre-built models and algorithms, which otherwise take a huge amount of resources if built from scratch. As these APIs get deployed for high-stakes applications, it's very important that they are robust to different ...
Jaydeep Borkar, Pin-Yu Chen
openaire   +2 more sources

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
openaire   +2 more sources

Detecting Audio Adversarial Examples in Automatic Speech Recognition Systems Using Decision Boundary Patterns

Journal of Imaging, 2022
Automatic Speech Recognition (ASR) systems are ubiquitous in various commercial applications. These systems typically rely on machine learning techniques for transcribing voice commands into text for further processing.
Wei Zong, Yang-Wai Chow, Willy Susilo, Jongkil Kim, Ngoc Thuy Le   +4 more
doaj   +1 more source

Adversarial Diversity and Hard Positive Generation

, 2016
State-of-the-art deep neural networks suffer from a fundamental problem - they misclassify adversarial examples formed by applying small perturbations to inputs.
Boult, Terrance E., Rozsa, Andras, Rudd, Ethan M.   +2 more
core   +1 more source

DroidEnemy: Battling adversarial example attacks for Android malware detection

Digital Communications and Networks, 2022
In recent years, we have witnessed a surge in mobile devices such as smartphones, tablets, smart watches, etc., most of which are based on the Android operating system. However, because these Android-based mobile devices are becoming increasingly popular,
Neha Bala, Aemun Ahmar, Wenjia Li, Fernanda Tovar, Arpit Battu, Prachi Bambarkar   +5 more
doaj   +1 more source