Results 81 to 90 of about 3,240,231 (313)

DP²O: Dual-Pair Direct Preference Optimization for Preference-Guided Adversarial Robustness in Vision Models

IEEE Access
Adversarial training remains the most effective empirical defense against adversarial examples, yet it is hindered by the high cost of inner maximization, the clean–robust accuracy trade-off, and the underutilization of information contained in ...
Ahmed Dawod Mohammed Ibrahum, Jang-Eui Hong   +1 more
doaj   +1 more source

Pareto Adversarial Robustness: Balancing Spatial Robustness and Sensitivity-based Robustness [PDF]

Adversarial robustness, which primarily comprises sensitivity-based robustness and spatial robustness, plays an integral part in achieving robust generalization. In this paper, we endeavor to design strategies to achieve universal adversarial robustness.
Sun, Ke, Li, Mingjie, Lin, Zhouchen
core   +1 more source

SQUEEZE TRAINING FOR ADVERSARIAL ROBUSTNESS [PDF]

, 2023
The vulnerability of deep neural networks (DNNs) to adversarial examples has attracted great attention in the machine learning community. The problem is related to non-flatness and non-smoothness of normally obtained loss landscapes.
Zuo, Wangmeng, Guo, Yiwen, Li, Qizhang, Chen, Hao   +3 more
core   +2 more sources

Functionalizing Micro‐to‐Mesoscopic Electrode Architectures for Regulating Electron Transfer Behaviors in Electrocatalysis

Advanced Functional Materials, EarlyView.
A systematic review is conducted to assess the influence of electrode architecture across micro‐ to mesoscopic length scales on electron‐transfer pathways in electrocatalysis. We discuss the structure‐activity relationships in electrocatalytic applications, including resource recovery and environmental remediation, and provide cost‐effective, efficient
Manshu Zhao, Jiaojiao Zhang, Shideng Yuan, Xinmeng Yu, Keming Wan, Xiaoguang Duan, Zhining Wang   +6 more
wiley   +1 more source

Chaos Theory and Adversarial Robustness

CoRR, 2022
14 pages, 6 ...
openaire   +2 more sources

On the Interplay of Convolutional Padding and Adversarial Robustness

, 2023
It is common practice to apply padding prior to convolution operations to preserve the resolution of feature-maps in Convolutional Neural Networks (CNN). While many alternatives exist, this is often achieved by adding a border of zeros around the inputs.
Gavrikov, Paul, Keuper, Janis
core   +1 more source

Self‐Assembled Monolayers in p–i–n Perovskite Solar Cells: Molecular Design, Interfacial Engineering, and Machine Learning–Accelerated Material Discovery

Advanced Materials, EarlyView.
This review highlights the role of self‐assembled monolayers (SAMs) in perovskite solar cells, covering molecular engineering, multifunctional interface regulation, machine learning (ML) accelerated discovery, advanced device architectures, and pathways toward scalable fabrication and commercialization for high‐efficiency and stable single‐junction and
Asmat Ullah, Ying Luo, Stefaan De Wolf
wiley   +1 more source

Manifold-driven decomposition for adversarial robustness

Frontiers in Computer Science
The adversarial risk of a machine learning model has been widely studied. Most previous studies assume that the data lie in the whole ambient space. We propose to take a new angle and take the manifold assumption into consideration.
Wenjia Zhang, Yikai Zhang, Xiaoling Hu, Yi Yao, Mayank Goswami, Chao Chen, Dimitris Metaxas   +6 more
doaj   +1 more source

Provably Robust Adversarial Examples

, 2020
International Conference on Learning Representations (ICLR 2022)
Dimitar Iliev Dimitrov, Gagandeep Singh 0001, Timon Gehr, Martin T. Vechev   +3 more
openaire   +4 more sources

Towards Adversarial Robustness of Deep Vision Algorithms [PDF]

, 2022
Deep learning methods have achieved great success in solving computer vision tasks, and they have been widely utilized in artificially intelligent systems for image processing, analysis, and understanding. However, deep neural networks have been shown to
Yan, Hanshu
core