Results 291 to 300 of about 5,380,268 (331)

The strength of Nesterov's accelerated gradient in boosting transferability of stealthy adversarial attacks. [PDF]

PLoS One
Lin C, Long S.
europepmc   +1 more source

AB jailbreaking - a novel hybrid framework for exploitation of adversarial vulnerabilities in LLMs. [PDF]

Sci Rep
Ahmad A, Naseer M, Qamar U, Alfuraidan M.   +3 more
europepmc   +1 more source

AI in Dermato-Oncology: Diagnostic Performance and Prompt-Injection Vulnerability of Vision-Language Models in Dermoscopic Skin Cancer Assessment. [PDF]

Cancers (Basel)
Güler I, Kraus A, Grieb G, Satir T, Eberz P, Stelling H.   +5 more
europepmc   +1 more source

Evaluation of Explainable Artificial Intelligence in IoT Intrusion Detection Systems Under DeepFool Adversarial Conditions. [PDF]

Sensors (Basel)
Munilla J, Khammas RM.
europepmc   +1 more source

Ensuring the integrity of AI models: a blockchain-based approach for protecting medical imaging training data. [PDF]

Sci Rep
Shinde R, Patil S, Kotecha K, Mishra S.
europepmc   +1 more source

Direction-aggregated Attack for Transferable Adversarial Examples [PDF]

ACM Journal on Emerging Technologies in Computing Systems, 2022
Deep neural networks are vulnerable to adversarial examples that are crafted by imposing imperceptible changes to the inputs. However, these adversarial examples are most successful in white-box settings where the model and its parameters are available ...
Tianjin Huang, Vlado Menkovski, Yulong Pei   +2 more
exaly   +2 more sources

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Adversarial Examples Are Not Easily Detected: Bypassing Ten Detection Methods

AISec@CCS, 2017
Neural networks are known to be vulnerable to adversarial examples: inputs that are close to natural inputs but classified incorrectly. In order to better understand the space of adversarial examples, we survey ten recent proposals that are designed for ...
Nicholas Carlini, D. Wagner
semanticscholar   +1 more source

Learning Universal Adversarial Perturbation by Adversarial Example

Proceedings of the AAAI Conference on Artificial Intelligence, 2022
Deep learning models have shown to be susceptible to universal adversarial perturbation (UAP), which has aroused wide concerns in the community. Compared with the conventional adversarial attacks that generate adversarial samples at the instance level, UAP can fool the target model for different instances with only a single perturbation, enabling us to
Maosen Li, Yanhua Yang, Kun Wei, Xu Yang 0019, Heng Huang 0001   +4 more
openaire   +1 more source

Interpreting Adversarial Examples in Deep Learning: A Review

ACM Computing Surveys, 2023
Deep learning technology is increasingly being applied in safety-critical scenarios but has recently been found to be susceptible to imperceptible adversarial perturbations.
Sicong Han, Chenhao Lin, Chao Shen, Qianqian Wang, X. Guan   +4 more
semanticscholar   +1 more source