Results 121 to 130 of about 107,198 (316)

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

ProDef-MDS: A Proactive Defense Mechanism Protecting Malware Detection Systems from Adversarial Attacks

Vietnam Journal of Computer Science
Malware threatens cybersecurity by enabling data theft, unauthorized access, and extortion. Traditional malware detection systems (MDS) struggle with the increasing volume and complexity of malware.
Nghi Hoang Khoa, Nguyen Hoang Duy, Le Vu Ca, Nguyen Huu Quyen, Van-Hau Pham, Phan The Duy   +5 more
doaj   +1 more source

Deepfake Cross-Model Defense Method Based on Generative Adversarial Network [PDF]

Jisuanji gongcheng
To reduce social risks caused by the abuse of deepfake technology, an active defense method against deep forgery based on a Generative Adversarial Network (GAN) is proposed. Adversarial samples are created by adding imperceptible perturbation to original
DAI Lei, CAO Lin, GUO Yanan, ZHANG Fan, DU Kangning
doaj   +1 more source

Adversarial Selection

SSRN Electronic Journal
In many institutional settings, <i>k</i> items are selected with the goal of representing the underlying distribution of claims, opinions, or characteristics in a large population. We study environments with two adversarial parties whose preferences over the selected items are commonly known and opposed.
Alma Cohen, Alon Klement, Zvika Neeman, Eilon Solan   +3 more
openaire   +2 more sources

Optimal online prediction in adversarial environments

, 2010
In many prediction problems, including those that arise in computer security and computational finance, the process generating the data is best modelled as an adversary with whom the predictor competes.
Bartlett, Peter L., Peter L. Bartlett
core   +1 more source

Deep Learning‐Assisted Coherent Raman Scattering Microscopy

Advanced Intelligent Discovery, EarlyView.
The analytical capabilities of coherent Raman scattering microscopy are augmented through deep learning integration. This synergistic paradigm improves fundamental performance via denoising, deconvolution, and hyperspectral unmixing. Concurrently, it enhances downstream image analysis including subcellular localization, virtual staining, and clinical ...
Jianlin Liu, Rujia Liu, Xinyan Zhang, Xun Chen, Shuhua Yue   +4 more
wiley   +1 more source

Adversarial Halftone QR Code

IEEE Access
Recent studies have shown that machine-learning models are vulnerable to adversarial attacks. Adversarial attacks are deliberate attempts to modify the input data of a machine learning model in a way that causes it to produce incorrect predictions.
Palakorn Kamnounsing, Karin Sumongkayothin, Prarinya Siritanawan, Kazunori Kotani   +3 more
doaj   +1 more source

Deep Learning‐Assisted Design of Mechanical Metamaterials

Advanced Intelligent Discovery, EarlyView.
This review examines the role of data‐driven deep learning methodologies in advancing mechanical metamaterial design, focusing on the specific methodologies, applications, challenges, and outlooks of this field. Mechanical metamaterials (MMs), characterized by their extraordinary mechanical behaviors derived from architected microstructures, have ...
Zisheng Zong, Pu Wen, Zhiping Chai, Dunxuan Wu, Han Ding, Zhigang Wu   +5 more
wiley   +1 more source

The Optimal Amount of Falsified Testimony [PDF]

An arbiter can decide a case on the basis of his priors or he can ask for further evidence from the two parties to the conflict. The parties may misrepresent evidence in their favor at a cost.
Winand Emons, Claude Fluet
core  

Statistical Feature-Based Detection of Adversarial Noise and Patch Attacks in Image and Deepfake Analysis

195208Adversarial attacks pose a significant threat to the reliability and trustworthiness of machine learning systems, particularly in image classification tasks like deepfake detection.
Bunzel, Niklas, Göller, Nicolas, Steinebach, Martin, Graner, Lukas, Frick, Raphael   +4 more
core   +1 more source