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Defense Against Adversarial AI [PDF]

Journal of Cognitive Human-Computer Interaction
The increasing prevalence of deep learning technology has paved the way for a new era of AI-powered capabilities, promising revolutionary advancements across various societal domains such as healthcare and autonomous vehicles.
Bhavani G., Soundarya S., Tejashwini V., Sumitha S.   +3 more
doaj   +1 more source

Knowing is Half the Battle: Enhancing Clean Data Accuracy of Adversarial Robust Deep Neural Networks via Dual-Model Bounded Divergence Gating

IEEE Access
Significant advances have been made in recent years in improving the robustness of deep neural networks, particularly under adversarial machine learning scenarios where the data has been contaminated to fool networks into making undesirable predictions ...
Hossein Aboutalebi, Mohammad Javad Shafiee, Chi-En Amy Tai, Alexander Wong   +3 more
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  

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

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  

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

A Multi-Model Hybrid Defense Approach Against White-box Adversarial Attacks in Computer Network Traffic

It is crucial to safeguard computer networks from evolving network security threats and unknown cyberattacks. An essential tool for protecting computer networks against unknown cyber threats is Network Intrusion Detection System (NIDS).
Khushnaseeb Roshan
core   +1 more source