Results 91 to 100 of about 505,564 (200)
DeeBBAA: A Benchmark Deep Black Box Adversarial Attack Against Cyber-Physical Power Systems
Cyber-physical infrastructure faces threats from evasive false data injection attacks that can significantly impact their security and performance. Adversarial attacks are a popular evasive false data injection threat model that generally targets AI/ML ...
Tushar, W +5 more
core +1 more source
An adversarial attack method based on pixel location characteristics
Deep learning techniques have been widely used in various fields. However, they face significant security challenges due to the existence of adversarial examples.
Zhao Qin +4 more
doaj +1 more source
Harmonic Adversarial Attack Method
Adversarial attacks find perturbations that can fool models into misclassifying images. Previous works had successes in generating noisy/edge-rich adversarial perturbations, at the cost of degradation of image quality. Such perturbations, even when they are small in scale, are usually easily spottable by human vision.
Wen Heng +2 more
openaire +2 more sources
Attack-Agnostic Adversarial Detection
The growing number of adversarial attacks in recent years gives attackers an advantage over defenders, as defenders must train detectors after knowing the types of attacks, and many models need to be maintained to ensure good performance in detecting any
Cheng, Jiaxin +3 more
core
Ctta: a novel chain-of-thought transfer adversarial attacks framework for large language models
Recent studies have indicated that large language models (LLMs) remain susceptible to adversarial attacks, despite enhanced robustness through the chain-of-thought (CoT) capability.
Xinxin Yue +3 more
doaj +1 more source
Link Prediction Adversarial Attack
Deep neural network has shown remarkable performance in solving computer vision and some graph evolved tasks, such as node classification and link prediction. However, the vulnerability of deep model has also been revealed by carefully designed adversarial examples generated by various adversarial attack methods.
Jinyin Chen +4 more
openaire +2 more sources
Linear Interpolation Method for Adversarial Attack [PDF]
Deep neural networks exhibit significant vulnerability in the face of adversarial examples and are prone to attacks.The construction of adversarial examples can be abstracted as an optimization problem that maximizes the objective function.How-ever ...
CHEN Jun, ZHOU Qiang, BAO Lei, TAO Qing
core +1 more source
Llm-ga: A gradient-based multi-label adversarial attack by large language models
Deep neural networks (DNNs) are highly sensitive to small, meticulously crafted perturbations, which have been utilized in adversarial attacks, threatening the reliability of DNNs in practical applications. Current adversarial attack methods rely heavily
Yujiang Liu +4 more
doaj +1 more source
Deep neural networks have achieved remarkable performance in remote sensing image (RSI) classification tasks. However, they remain vulnerable to adversarial attack.
Xiyu Peng, Jingyi Zhou, Xiaofeng Wu
doaj +1 more source
With the development of artificial intelligence, machine learning algorithms and deep learning algorithms are widely applied to attack detection models. Adversarial attacks against artificial intelligence models become inevitable problems when there is a
Yong Fang +3 more
doaj +1 more source

