Results 81 to 90 of about 27,717 (260)

An Audio Watermarking Algorithm Based on Adversarial Perturbation

Applied Sciences
Recently, deep learning has been gradually applied to digital watermarking, which avoids the trouble of hand-designing robust transforms in traditional algorithms.
Shiqiang Wu, Jie Liu, Ying Huang, Hu Guan, Shuwu Zhang   +4 more
doaj   +1 more source

Adversarial attack and defense in reinforcement learning-from AI security view

Cybersecurity, 2019
Reinforcement learning is a core technology for modern artificial intelligence, and it has become a workhorse for AI applications ranging from Atrai Game to Connected and Automated Vehicle System (CAV).
Tong Chen, Jiqiang Liu, Yingxiao Xiang, Wenjia Niu, Endong Tong, Zhen Han   +5 more
doaj   +1 more source

Intriguing Properties of Adversarial Examples

CoRR, 2017
17 ...
Ekin Dogus Cubuk, Barret Zoph, Samuel S. Schoenholz, Quoc V. Le   +3 more
openaire   +3 more sources

Ovarian Cancer Diagnosis and Chemoresistance Prediction Model Based on cfRNA Molecular Signature

Advanced Science, EarlyView.
A deep learning model analyzes cfRNA profiles extracted from the blood of OVCA patients. This innovative approach distinguishes OVCA from healthy controls with high accuracy. Crucially, it reliably predicts patient response to chemotherapy (sensitive versus resistant subgroups).
Qinhao Guo, Yangyang Zhang, Yongcheng Jin, Siwei Deng, Yongqi Chen, Zheng Feng, Hao Wen, Liu Wang, Yilin Li, Fanghong Ou, Yong Shen, Haiming Li, Tianyao Zhou, Xingzhu Ju, Xiaohua Wu   +14 more
wiley   +1 more source

AEFOP: Adversarial Energy Field Optimization for Adversarial Example Purification

Applied Sciences
As AI-driven educational systems increasingly rely on deep neural networks, their vulnerability to adversarial perturbations raises concerns about assessment integrity, fairness, and reliability.
Heqi Peng, Shengpeng Xiao, Yuanfang Guo
doaj   +1 more source

Generating Natural Adversarial Examples

CoRR, 2017
Due to their complex nature, it is hard to characterize the ways in which machine learning models can misbehave or be exploited when deployed. Recent work on adversarial examples, i.e. inputs with minor perturbations that result in substantially different model predictions, is helpful in evaluating the robustness of these models by exposing the ...
Zhengli Zhao, Dheeru Dua, Sameer Singh 0001   +2 more
openaire   +3 more sources

Hardware‐Attentive Programmable Fourier Ptychography Enables Task‐Adaptive Label‐Free Virtual Staining

Advanced Science, EarlyView.
Task‐adaptive programmable optics enables label‐free virtual staining through optical‐attention‐guided acquisition and reconstruction. By optimizing wavelength, illumination angle, exposure time, and imaging depth, the framework learns task‐relevant optical measurements, generating clinically interpretable virtual stains with improved fidelity, non ...
Tianyue He, Wenyi Jing, Le Zhang, Qican Zhang, Tingdong Kou, Xin He, Yan Qiu, Yang Lu, Jian Cui, Yongfu Wen, Zhenrong Zheng, Hongying Zhang, Dayong Jin, Junfei Shen   +13 more
wiley   +1 more source

A New Kind of Adversarial Example

CoRR, 2022
Almost all adversarial attacks are formulated to add an imperceptible perturbation to an image in order to fool a model. Here, we consider the opposite which is adversarial examples that can fool a human but not a model. A large enough and perceptible perturbation is added to an image such that a model maintains its original decision, whereas a human ...
openaire   +2 more sources

StackingNet: Collective Inference Across Independent AI Foundation Models

Advanced Science, EarlyView.
ABSTRACT Artificial intelligence (AI) built on large foundation models has transformed language understanding, computer vision, and reasoning, yet these systems remain isolated and cannot readily share their capabilities. Coordinating the complementary strengths of independently developed, black‐box foundation models is essential for trustworthy ...
Siyang Li, Chenhao Liu, Dongrui Wu, Zhigang Zeng, Lieyun Ding   +4 more
wiley   +1 more source

Adversarial robustness analysis of LiDAR-included models in autonomous driving

High-Confidence Computing
In autonomous driving systems, perception is pivotal, relying chiefly on sensors like LiDAR and cameras for environmental awareness. LiDAR, celebrated for its detailed depth perception, is being increasingly integrated into autonomous vehicles.
Bo Yang, Zizhi Jin, Yushi Cheng, Xiaoyu Ji, Wenyuan Xu   +4 more
doaj   +1 more source