Results 51 to 60 of about 177,286 (215)

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

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

Robust Audio Adversarial Example for a Physical Attack

, 2019
We propose a method to generate audio adversarial examples that can attack a state-of-the-art speech recognition model in the physical world. Previous work assumes that generated adversarial examples are directly fed to the recognition model, and is not ...
Sakuma, Jun, Yakura, Hiromu
core   +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
openaire   +2 more sources

Adversarial Training for Free!

, 2019
Adversarial training, in which a network is trained on adversarial examples, is one of the few defenses against adversarial attacks that withstands strong attacks.
Davis, Larry S., Dickerson, John, Ghiasi, Amin, Goldstein, Tom, Najibi, Mahyar, Shafahi, Ali, Studer, Christoph, Taylor, Gavin, Xu, Zheng   +8 more
core   +1 more source

Downstream-agnostic Adversarial Examples

2023 IEEE/CVF International Conference on Computer Vision (ICCV), 2023
This paper has been accepted by the International Conference on Computer Vision (ICCV '23, October 2--6, 2023, Paris, France)
Ziqi Zhou 0001, Shengshan Hu, Ruizhi Zhao, Qian Wang 0002, Leo Yu Zhang, Junhui Hou, Hai Jin 0001   +6 more
openaire   +2 more sources

Maxwell’s Demon in MLP-Mixer: towards transferable adversarial attacks

Cybersecurity
Models based on MLP-Mixer architecture are becoming popular, but they still suffer from adversarial examples. Although it has been shown that MLP-Mixer is more robust to adversarial attacks compared to convolutional neural networks (CNNs), there has been
Haoran Lyu, Yajie Wang, Yu-an Tan, Huipeng Zhou, Yuhang Zhao, Quanxin Zhang   +5 more
doaj   +1 more source

Assessing Optimizer Impact on DNN Model Sensitivity to Adversarial Examples

IEEE Access, 2019
Deep Neural Networks (DNNs) have been gaining state-of-the-art achievement compared with many traditional Machine Learning (ML) models in diverse fields. However, adversarial examples challenge the further deployment and application of DNNs. Analysis has
Yixiang Wang, Jiqiang Liu, Jelena Misic, Vojislav B. Misic, Shaohua Lv, Xiaolin Chang   +5 more
doaj   +1 more source

A Novel Adversarial Example Detection Method Based on Frequency Domain Reconstruction for Image Sensors

Sensors
Convolutional neural networks (CNNs) have been extensively used in numerous remote sensing image detection tasks owing to their exceptional performance.
Shuaina Huang, Zhiyong Zhang, Bin Song
doaj   +1 more source