Results 51 to 60 of about 992 (214)

A Mask-Based Adversarial Defense Scheme

Algorithms, 2022
Adversarial attacks hamper the functionality and accuracy of deep neural networks (DNNs) by meddling with subtle perturbations to their inputs. In this work, we propose a new mask-based adversarial defense scheme (MAD) for DNNs to mitigate the negative ...
Weizhen Xu, Chenyi Zhang, Fangzhen Zhao, Liangda Fang   +3 more
doaj   +1 more source

Adversarial Example Defenses: Ensembles of Weak Defenses are not Strong

CoRR, 2017
Ongoing research has proposed several methods to defend neural networks against adversarial examples, many of which researchers have shown to be ineffective. We ask whether a strong defense can be created by combining multiple (possibly weak) defenses. To answer this question, we study three defenses that follow this approach. Two of these are recently
Warren He, James Wei, Xinyun Chen, Nicholas Carlini, Dawn Song   +4 more
openaire   +3 more sources

Defense-VAE: A Fast and Accurate Defense Against Adversarial Attacks [PDF]

, 2020
Deep neural networks (DNNs) have been enormously successful across a variety of prediction tasks. However, recent research shows that DNNs are particularly vulnerable to adversarial attacks, which poses a serious threat to their applications in security-sensitive systems.
Xiang Li 0080, Shihao Ji 0001
openaire   +2 more sources

Navigating Ethical Minefields: Micro‐Foundations of Corporate Political Work in Ethical and Sustainable Business Performance

Business Strategy and the Environment, EarlyView.
ABSTRACT In an era of rising geopolitical tensions and environmental instability, corporate political activities have become increasingly intertwined with ethical challenges and sustainability requirements. This study investigates the influence of environmental dynamics and corporate ethical responsibility on interorganizational conflict and ...
David Yulong Liu, Justin Zuopeng Zhang, Jimmy Sun, Muhammad Mustafa Kamal, Bhumika Gupta   +4 more
wiley   +1 more source

Mape: defending against transferable adversarial attacks using multi-source adversarial perturbations elimination

Complex & Intelligent Systems
Neural networks are vulnerable to meticulously crafted adversarial examples, leading to high-confidence misclassifications in image classification tasks. Due to their consistency with regular input patterns and the absence of reliance on the target model
Xinlei Liu, Jichao Xie, Tao Hu, Peng Yi, Yuxiang Hu, Shumin Huo, Zhen Zhang   +6 more
doaj   +1 more source

You Can’t Fool All the Models: Detect Adversarial Samples via Pruning Models

IEEE Access, 2021
Many adversarial attack methods have investigated the security issue of deep learning models. Previous works on detecting adversarial samples show superior in accuracy but consume too much memory and computing resources.
Renxuan Wang, Zuohui Chen, Hui Dong, Qi Xuan   +3 more
doaj   +1 more source

Efficient Defenses Against Adversarial Attacks [PDF]

Proceedings of the 10th ACM Workshop on Artificial Intelligence and Security, 2017
Following the recent adoption of deep neural networks (DNN) accross a wide range of applications, adversarial attacks against these models have proven to be an indisputable threat. Adversarial samples are crafted with a deliberate intention of undermining a system. In the case of DNNs, the lack of better understanding of their working has prevented the
Valentina Zantedeschi, Maria-Irina Nicolae, Ambrish Rawat   +2 more
openaire   +2 more sources

Beyond the Adversarial Rivalry: A Developmental Rights‐Based Model for Minor‐on‐Minor Crime, Part 1

Behavioral Sciences &the Law, EarlyView.
ABSTRACT When children harm children, the conventional victim–perpetrator framework is ill‐equipped to address the developmental, relational, and institutional complexities involved. While juvenile justice scholarship increasingly emphasizes rehabilitation, and victims' rights literature has advanced child‐sensitive protection, minor‐on‐minor offending
Tali Gal, Ruthy Lowenstein Lazar
wiley   +1 more source

Generative Adversarial Trainer: Defense to Adversarial Perturbations with GAN

CoRR, 2017
We propose a novel technique to make neural network robust to adversarial examples using a generative adversarial network. We alternately train both classifier and generator networks. The generator network generates an adversarial perturbation that can easily fool the classifier network by using a gradient of each image.
Hyeungill Lee, Sungyeob Han, Jungwoo Lee 0001   +2 more
openaire   +2 more sources

Beyond the Adversarial Rivalry: A Developmental Rights‐Based Model for Minor‐on‐Minor Crime, Part 2

Behavioral Sciences &the Law, EarlyView.
ABSTRACT When children harm children, the conventional victim–perpetrator framework is ill‐equipped to address the developmental, relational, and institutional complexities involved. While juvenile justice scholarship increasingly emphasizes rehabilitation, and victims' rights literature has advanced child‐sensitive protections, minor‐on‐minor ...
Tali Gal, Ruthy Lowenstein Lazar
wiley   +1 more source