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Child &Family Social Work, Volume 31, Issue 3, Page 1676-1687, August 2026.ABSTRACT Fifty years after the formation of England's child protection system, the country continues to reel from new instances of high‐profile child death tragedies where children have been harmed despite practitioners complying with the processes and procedures designed to protect them.
Ciarán Murphy, Michael Murphy
wiley +1 more source
Children's Agency in Contact Disputes: Navigating Protection, Participation and Alienation
Child &Family Social Work, Volume 31, Issue 3, Page 1761-1772, August 2026.ABSTRACT This article examines how children's agency is framed, constrained and sometimes co‐opted within contested child arrangement proceedings, particularly in the context of alienation and coercive behaviours. Drawing on qualitative interviews with legal professionals in Northern Ireland, the study explores how statutory interventions, though well ...
Mairead McCormack
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Computational Intelligence, Volume 42, Issue 4, August 2026.ABSTRACT Large language models are increasingly used as programming assistants, but their security behavior remains uneven: they may generate code with vulnerable patterns, and they may provide actionable help for malicious requests. This paper introduces AlquistCoder, a compact 3.8B‐parameter coding assistant designed to address both risks through ...
Ondřej Kobza +6 more
wiley +1 more source
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Adversarial Attacks and Defenses on Graphs
ACM SIGKDD Explorations Newsletter, 2021Deep neural networks (DNNs) have achieved significant performance in various tasks. However, recent studies have shown that DNNs can be easily fooled by small perturbation on the input, called adversarial attacks.
Wei Jin 0009 +6 more
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Sinkhorn Adversarial Attack and Defense
IEEE Transactions on Image Processing, 2022Adversarial attacks have been extensively investigated in the recent past. Quite interestingly, a majority of these attacks primarily work in the lp space. In this work, we propose a novel approach for generating adversarial samples using Wasserstein distance.
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Adversarial Attacks and Defenses
Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2020Deep neural networks (DNN) have achieved unprecedented success in numerous machine learning tasks in various domains. However, the existence of adversarial examples leaves us a big hesitation when applying DNN models on safety-critical tasks such as autonomous vehicles and malware detection.
Han Xu 0002 +3 more
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Multi-view Defense with Adversarial Autoencoders
2021 International Joint Conference on Neural Networks (IJCNN), 2021In view of the vulnerability of multi-view deep models to adversarial perturbation, this paper designs two kinds of multi-view adversarial autoencoders (MAAEs). We first propose the MAAE1 defense, in which each view is used to train the corresponding single-view adversarial autoencoders separately, and then the reconstructed output is fed into the ...
Xuli Sun, Shiliang Sun
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DeepRobust: a Platform for Adversarial Attacks and Defenses
Proceedings of the AAAI Conference on Artificial Intelligence, 2021DeepRobust is a PyTorch platform for generating adversarial examples and building robust machine learning models for different data domains. Users can easily evaluate the attack performance against different defense methods with DeepRobust and get performance analyzing visualization.
Yaxin Li 0001 +3 more
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MTD-AD: Moving Target Defense as Adversarial Defense
IEEE Transactions on Dependable and Secure ComputingNetwork Intrusion Detection Systems (NIDSes) are increasingly incorporating Machine Learning (ML) and Deep Learning (DL) algorithms for detecting network intrusions. However, ML/DL algorithms are susceptible to adversarial examples, which can lead to the misclassification of input data.
Ke He +2 more
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Variational Adversarial Defense: A Bayes Perspective for Adversarial Training
IEEE Transactions on Pattern Analysis and Machine IntelligenceVarious methods have been proposed to defend against adversarial attacks. However, there is a lack of enough theoretical guarantee of the performance, thus leading to two problems: First, deficiency of necessary adversarial training samples might attenuate the normal gradient's back-propagation, which leads to overfitting and gradient masking ...
Chenglong Zhao +5 more
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