Adversarial attack - Open Access .click

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CoRR, 2018
Deep neural network has shown remarkable performance in solving computer vision and some graph evolved tasks, such as node classification and link prediction. However, the vulnerability of deep model has also been revealed by carefully designed adversarial examples generated by various adversarial attack methods.
Jinyin Chen +4 more
openaire +2 more sources

Rethinking Classifier and Adversarial Attack

CoRR, 2022
Various defense models have been proposed to resist adversarial attack algorithms, but existing adversarial robustness evaluation methods always overestimate the adversarial robustness of these models (i.e., not approaching the lower bound of robustness). To solve this problem, this paper uses the proposed decouple space method to divide the classifier
Youhuan Yang +6 more
openaire +2 more sources

Object-Attentional Untargeted Adversarial Attack

, 2022
Deep neural networks are facing severe threats from adversarial attacks. Most existing black-box attacks fool target model by generating either global perturbations or local patches.
Wang, Yuan-Gen, Zhou, Chao, Zhu, Guopu
core

Enhancing Adversarial Attacks via Parameter Adaptive Adversarial Attack

CoRR
In recent times, the swift evolution of adversarial attacks has captured widespread attention, particularly concerning their transferability and other performance attributes. These techniques are primarily executed at the sample level, frequently overlooking the intrinsic parameters of models.
Zhibo Jin +6 more
openaire +2 more sources

Adversarial accuracy per th for L_∞ attack.

, 2022
Adversarial accuracy per th for L∞ attack.
Danilo Vasconcellos Vargas (12418891), Shashank Kotyan (12418888) +1 more
core +1 more source

Adversarial attacks on an oblivious recommender [PDF]

Proceedings of the 13th ACM Conference on Recommender Systems, 2019
Can machine learning models be easily fooled? Despite the recent surge of interest in learned adversarial attacks in other domains, in the context of recommendation systems this question has mainly been answered using hand-engineered fake user profiles. This paper attempts to reduce this gap.
Konstantina Christakopoulou, Arindam Banerjee 0001 +1 more
openaire +2 more sources

Multi-Level Chinese Adversarial Example Generation Method Based on Glyph and Semantic [PDF]

Jisuanji gongcheng
Deep neural network language models are vulnerable to adversarial example attacks during application. To address this issue, adversarial examples are typically generated by adding minor perturbations to the original samples to mislead the model into ...
SUN Yu, WANG Hongjie, DU Yanhui, LIU Nan
doaj +1 more source

Adversarial Attacks and Defences: A Survey

CoRR, 2018
Deep learning has emerged as a strong and efficient framework that can be applied to a broad spectrum of complex learning problems which were difficult to solve using the traditional machine learning techniques in the past. In the last few years, deep learning has advanced radically in such a way that it can surpass human-level performance on a number ...
Anirban Chakraborty 0003 +4 more
openaire +2 more sources

Adversarial accuracy per th for L₀ attack.

, 2022
Adversarial accuracy per th for L0 attack.
Danilo Vasconcellos Vargas (12418891), Shashank Kotyan (12418888) +1 more
core +1 more source

SURVEY OF ADVERSARIAL ATTACKS AND DEFENSE AGAINST ADVERSARIAL ATTACKS

Darpan International Research Analysis
In recent years, the fields of Artificial Intelligence (AI) and Deep learning (DL) techniques along with Neural Networks (NNs) have shown great progress and scope for future research. Along with all the developments comes the threats and security vulnerabilities to Neural Networks and AI models. A few fabricated inputs/samples can lead to deviations in
Akshat Jain +3 more
openaire +1 more source

adversarial attacks
adversarial example
machine learning

adversarial examples
deep learning
object detection

generative adversarial networks