Results 71 to 80 of about 177,286 (215)

A Gradual Adversarial Training Method for Semantic Segmentation

Remote Sensing
Deep neural networks (DNNs) have achieved great success in various computer vision tasks. However, they are susceptible to artificially designed adversarial perturbations, which limit their deployment in security-critical applications.
Yinkai Zan, Pingping Lu, Tingyu Meng
doaj   +1 more source

CommanderUAP: a practical and transferable universal adversarial attacks on speech recognition models

Cybersecurity
Most of the adversarial attacks against speech recognition systems focus on specific adversarial perturbations, which are generated by adversaries for each normal example to achieve the attack.
Zheng Sun, Jinxiao Zhao, Feng Guo, Yuxuan Chen, Lei Ju   +4 more
doaj   +1 more source

Intriguing Properties of Adversarial Examples

CoRR, 2017
17 ...
Ekin Dogus Cubuk, Barret Zoph, Samuel S. Schoenholz, Quoc V. Le   +3 more
openaire   +3 more sources

Adversarial Robustness by One Bit Double Quantization for Visual Classification

IEEE Access, 2019
In this paper, we propose a novel robust visual classification framework that uses double quantization (dquant) to defend against adversarial examples in a specific attack scenario called “subsequent adversarial examples” where test images ...
Maungmaung Aprilpyone, Yuma Kinoshita, Hitoshi Kiya   +2 more
doaj   +1 more source

Does Physical Adversarial Example Really Matter to Autonomous Driving? Towards System-Level Effect of Adversarial Object Evasion Attack [PDF]

, 2023
Ningfei Wang, Yunpeng Luo, Takami Sato, Kaidi Xu, Qi Alfred Chen   +4 more
openalex   +1 more source

Generating Natural Adversarial Examples

CoRR, 2017
Due to their complex nature, it is hard to characterize the ways in which machine learning models can misbehave or be exploited when deployed. Recent work on adversarial examples, i.e. inputs with minor perturbations that result in substantially different model predictions, is helpful in evaluating the robustness of these models by exposing the ...
Zhengli Zhao, Dheeru Dua, Sameer Singh 0001   +2 more
openaire   +3 more sources

Image Classification Adversarial Example Defense Method Based on Conditional Diffusion Model [PDF]

Jisuanji gongcheng
Deep-learning models have achieved impressive results in fields such as image classification; however, they remain vulnerable to interference and threats from adversarial examples.
CHEN Zimin, GUAN Zhitao
doaj   +1 more source

Contrasting Human- and Machine-Generated Word-Level Adversarial Examples for Text Classification [PDF]

, 2021
Maximilian Mozes, Max Bartolo, Pontus Stenetorp, Bennett Kleinberg, Lewis D. Griffin   +4 more
openalex   +1 more source

Game Theoretic Mixed Experts for Combinational Adversarial Machine Learning

IEEE Access
Recent advances in adversarial machine learning have shown that defenses previously considered robust are actually susceptible to adversarial attacks which are specifically customized to target their weaknesses.
Kaleel Mahmood, Ethan Rathbun, Ronak Sahu, Marten Van Dijk, Sohaib Ahmad, Caiwen Ding   +5 more
doaj   +1 more source

Trace-Norm Adversarial Examples

CoRR, 2020
White box adversarial perturbations are sought via iterative optimization algorithms most often minimizing an adversarial loss on a $l_p$ neighborhood of the original image, the so-called distortion set. Constraining the adversarial search with different norms results in disparately structured adversarial examples.
Ehsan Kazemi 0003, Thomas Kerdreux, Liqiang Wang 0001   +2 more
openaire   +2 more sources