Results 91 to 100 of about 852,288 (193)

Image super-resolution as a defense against adversarial attacks

, 2020
Convolutional Neural Networks have achieved significant success across multiple computer vision tasks. However, they are vulnerable to carefully crafted, human-imperceptible adversarial noise patterns which constrain their deployment in critical security-
Mustafa, Aamir, Shao, Ling, Hayat, Munawar; id_orcid, Khan, Salman H., Shen, Jianbing   +4 more
core   +1 more source

Time-Constrained Adversarial Defense in IoT Edge Devices through Kernel Tensor Decomposition and Multi-DNN Scheduling. [PDF]

Sensors (Basel), 2022
Kim M, Joo S.
europepmc   +1 more source

RGB-depth salient object detection technology based on multi-level features and adversarial attacks

Discover Artificial Intelligence
Salient object detection in complex scenes suffers from insufficient cross-level feature interaction and vulnerability to adversarial perturbations. Existing RGB-D methods mainly focus on feature fusion but lack systematic adversarial defense. To address
Ruichao Xiao
doaj   +1 more source

Research on adversarial attack and defense of photovoltaic power prediction

Dianzi Jishu Yingyong
Deep neural networks have been widely used in photovoltaic power prediction, but they are vulnerable to adversarial attacks. In order to improve the robustness of the prediction model, an adversarial attack algorithm based on fast gradient sign method ...
Zhou Wang
doaj   +1 more source

IDEA: Invariant Defense for Graph Adversarial Robustness [PDF]

Despite the success of graph neural networks (GNNs), their vulnerability to adversarial attacks poses tremendous challenges for practical applications.
Shen, Huawei, Tao, Shuchang, Cao, Qi, Wu, Yunfan, Cheng, Xueqi, Xu, Bingbing   +5 more
core  

Enhancing Resilience Against Adversarial Attacks of Deep Neural Networks Using Efficient Two-Step Adversarial Defense [PDF]

, 2019
In recent years, deep neural networks have demonstrated outstanding performance in many machine learning tasks. However, researchers have discovered that these state-of-the-art models are vulnerable to adversarial examples: legitimate examples added by ...
Chang, Ting-Jui
core   +1 more source

CMDN: Pre-Trained Visual Representations Boost Adversarial Robustness for UAV Tracking

Drones
Visual object tracking is widely adopted to unmanned aerial vehicle (UAV)-related applications, which demand reliable tracking precision and real-time performance.
Ruilong Yu, Zhewei Wu, Qihe Liu, Shijie Zhou, Min Gou, Bingchen Xiang   +5 more
doaj   +1 more source

Detection and Defense: Student-Teacher Network for Adversarial Robustness

IEEE Access
Defense against adversarial attacks is critical for the reliability and safety of deep neural networks (DNNs). Current state-of-the-art defense methods achieve significant robustness against adversarial attacks.
Kyoungchan Park, Pilsung Kang
doaj   +1 more source

Generalization of Defense Effects Learned from a Single Adversarial Attack

Computation
Adversarial attacks misled deep neural networks by injecting perturbations into input images. Training networks with adversarial examples defended against adversarial attacks. However, training with specific adversarial examples only defended against the
Dongxian Niu, Lin Shi
doaj   +1 more source

Adversarial Attacks and Defenses in Fault Detection and Diagnosis: A Comprehensive Benchmark on the Tennessee Eastman Process

IEEE Open Journal of the Industrial Electronics Society
Integrating machine learning into Automated Control Systems (ACS) enhances decision-making in industrial process management. One of the limitations to the widespread adoption of these technologies in industry is the vulnerability of neural networks to ...
Vitaliy Pozdnyakov, Aleksandr Kovalenko, Ilya Makarov, Mikhail Drobyshevskiy, Kirill Lukyanov   +4 more
doaj   +1 more source