Results 131 to 140 of about 17,780 (281)
Research on adversarial attack and defense of photovoltaic power prediction
Dianzi Jishu YingyongDeep neural networks have been widely used in photovoltaic power prediction, but they are vulnerable to adversarial attacks. In order to improve the robustness of the prediction model, an adversarial attack algorithm based on fast gradient sign method ...
Zhou Wang
doaj +1 more source
The Impact of Simultaneous Adversarial Attacks on Robustness of Medical Image Analysis
Deep learning models are widely used in healthcare systems. However, deep learning models are vulnerable to attacks themselves. Significantly, due to the black-box nature of the deep learning model, it is challenging to detect attacks.
Rahman, Saifur +5 more
core +1 more source
‘Pro‐Germans in the Pulpits’: The Queensland Presbyterian Church and the Great War
Journal of Religious History, EarlyView.During World War I, Protestant churches in Australia, on the whole, enthusiastically supported the war effort. The Queensland Presbyterian Church was a significant exception. This study analyses discord and tensions among its clergymen about what constituted an appropriate response to the war.
Mark Cryle
wiley +1 more source
Adversarial Attacks on Data Attribution
CoRRAccepted at the 13th International Conference on Learning Representations (ICLR 2025)
Xinhe Wang 0001 +3 more
openaire +3 more sources
CycleGAN-Gradient Penalty for Enhancing Android Adversarial Malware Detection in Gray Box Setting
IEEE AccessAdversarial attacks pose significant threats to Android malware detection by undermining the effectiveness of machine learning-based systems. The rapid increase in Android apps complicates the management of malicious software that can compromise user ...
Fabrice Setephin Atedjio +4 more
doaj +1 more source
Adversarial Attacks on Transformers-Based Malware Detectors
, 2022 Signature-based malware detectors have proven to be insufficient as even a small change in malignant executable code can bypass these signature-based detectors. Many machine learning-based models have been proposed to efficiently detect a wide variety of
Patil, Heramb +3 more
core
Criminology, EarlyView.Abstract Research on how delinquent peer associations affect individuals’ life courses is limited. This paper addresses this gap by examining delinquent peer network characteristics and their impact on offending trajectories through social network analysis (SNA) and group‐based trajectory modeling (GBTM).
Daniel Trovato
wiley +1 more source
KNN-guided Adversarial Attacks [PDF]
, 2020 In the last decade, we have witnessed a renaissance of Deep Learning models. Nowadays, they are widely used in industrial as well as scientific fields, and noticeably, these models reached super-human per-formances on specific tasks such as image classification.
Massoli FV, Falchi F, Amato G
openaire +1 more source
Tricking Adversarial Attacks To Fail
CoRR, 2020 Recent adversarial defense approaches have failed. Untargeted gradient-based attacks cause classifiers to choose any wrong class. Our novel white-box defense tricks untargeted attacks into becoming attacks targeted at designated target classes. From these target classes, we can derive the real classes.
openaire +2 more sources
Increasing the Robustness of Image Quality Assessment Models Through Adversarial Training
TechnologiesThe adversarial robustness of image quality assessment (IQA) models to adversarial attacks is emerging as a critical issue. Adversarial training has been widely used to improve the robustness of neural networks to adversarial attacks, but little in-depth
Anna Chistyakova +6 more
doaj +1 more source