Results 131 to 140 of about 2,268,403 (339)

Adversarially Robust Deepfake Detection via Adversarial Feature Similarity Learning

MMM 2024 ...
Sarwar Khan, Jun-Cheng Chen, Wen-Hung Liao, Chu-Song Chen   +3 more
openaire   +2 more sources

Adversarial Robustness Toolbox v1.0.0

, 2018
34 ...
Nicolae, Maria-Irina, Sinn, Mathieu, Tran, Minh Ngoc, Buesser, Beat, Rawat, Ambrish, Wistuba, Martin, Zantedeschi, Valentina, Baracaldo, Nathalie, Chen, Bryant, Ludwig, Heiko, Molloy, Ian M., Edwards, Ben   +11 more
openaire   +2 more sources

CrossMatAgent: AI‐Assisted Design of Manufacturable Metamaterial Patterns via Multi‐Agent Generative Framework

Advanced Intelligent Discovery, EarlyView.
CrossMatAgent is a multi‐agent framework that combines large language models and diffusion‐based generative AI to automate metamaterial design. By coordinating task‐specific agents—such as describer, architect, and builder—it transforms user‐provided image prompts into high‐fidelity, printable lattice patterns.
Jie Tian, Martin Taylor Sobczak, Dhanush Patil, Jixin Hou, Lin Pang, Arunachalam Ramanathan, Libin Yang, Xianyan Chen, Yuval Golan, Xiaoming Zhai, Hongyue Sun, Kenan Song, Xianqiao Wang   +12 more
wiley   +1 more source

Three-Dimensional Reconstruction Pre-Training as a Prior to Improve Robustness to Adversarial Attacks and Spurious Correlation

Entropy
Ensuring robustness of image classifiers against adversarial attacks and spurious correlation has been challenging. One of the most effective methods for adversarial robustness is a type of data augmentation that uses adversarial examples during training.
Yutaro Yamada, Fred Weiying Zhang, Yuval Kluger, Ilker Yildirim   +3 more
doaj   +1 more source

Inverse Design of Alloys via Generative Algorithms: Optimization and Diffusion within Learned Latent Space

Advanced Intelligent Discovery, EarlyView.
This work presents a novel generative artificial intelligence (AI) framework for inverse alloy design through operations (optimization and diffusion) within learned compact latent space from variational autoencoder (VAE). The proposed work addresses challenges of limited data, nonuniqueness solutions, and high‐dimensional spaces.
Mohammad Abu‐Mualla, Ellis Crabtree, Fredrick Michael, Yayue Pan, Jida Huang   +4 more
wiley   +1 more source

Low-Pass Image Filtering to Achieve Adversarial Robustness. [PDF]

Sensors (Basel), 2023
Ziyadinov V, Tereshonok M.
europepmc   +1 more source

Artificial Intelligence for Bone: Theory, Methods, and Applications

Advanced Intelligent Discovery, EarlyView.
Advances in artificial intelligence (AI) offer the potential to improve bone research. The current review explores the contributions of AI to pathological study, biomarker discovery, drug design, and clinical diagnosis and prognosis of bone diseases. We envision that AI‐driven methodologies will enable identifying novel targets for drugs discovery. The
Dongfeng Yuan, Haicang Zhang, Liping Tong, Di Chen   +3 more
wiley   +1 more source

On Adversarial Robust Generalization of DNNs for Remote Sensing Image Classification

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Deep neural networks (DNNs)-based deep learning is an important technical support in the task of remote sensing image classification. But DNNs are susceptible to adversarial attacks.
Wei Xue, Yonghao Wang, Shaoquan Zhang, Xiao Zheng, Ping Zhong   +4 more
doaj   +1 more source

Exploring Adversarial Robustness of LiDAR Semantic Segmentation in Autonomous Driving. [PDF]

Sensors (Basel), 2023
Mahima KTY, Perera A, Anavatti S, Garratt M.   +3 more
europepmc   +1 more source

Adversarially Robust Decision Transformer

Advances in Neural Information Processing Systems 37
Accepted to NeurIPS ...
Tang, Xiaohang, Marques, Afonso, Kamalaruban, Parameswaran, Bogunovic, Ilija   +3 more
openaire   +2 more sources