Results 21 to 30 of about 148,046 (305)
Modeling Adversarial Noise for Adversarial Training
, 2021 Deep neural networks have been demonstrated to be vulnerable to adversarial noise, promoting the development of defense against adversarial attacks. Motivated by the fact that adversarial noise contains well-generalizing features and that the relationship between adversarial data and natural data can help infer natural data and make reliable ...
Dawei Zhou 0004 +3 more
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Broadening our understanding of adversarial growth: The contribution of narrative methods [PDF]
, 2023 After adversity, individuals sometimes report adversarial growth - positive changes in their identity, relationships, and worldviews. We examined how narrative methods enhanced understanding of adversarial growth compared to standard questionnaires ...
Adler, Jonathan M. +14 more
core +1 more source
Sistemas de manejo, preinstalación, instalación y resolución de audiencias en el Ecuador
Revista Científica Ciencia y Tecnología, 2019 Los modelos adversariales fueron instaurados en el Ecuador en todas sus materias. Un modelo de justicia basado en audiencias requiere a un Juez preparado no solo para conducir de forma adecuada una audiencia, sino también, una preparación para poder ...
Miguel Eduardo Costaín Vásquez +1 more
doaj +1 more source
Applied Sciences, 2023 While Machine Learning has become the holy grail of modern-day computing, it has many security flaws that have yet to be addressed and resolved. Adversarial attacks are one of these security flaws, in which an attacker appends noise to data samples that ...
Hiskias Dingeto, Juntae Kim
doaj +1 more source
IEEE Transactions on Cybernetics, 2022 16pages,9 figures ...
Chenghui Shi +6 more
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Clustering Approach for Detecting Multiple Types of Adversarial Examples
Sensors, 2022 With intentional feature perturbations to a deep learning model, the adversary generates an adversarial example to deceive the deep learning model.
Seok-Hwan Choi +3 more
doaj +1 more source
Adversarial classification [PDF]
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining, 2004 Essentially all data mining algorithms assume that the data-generating process is independent of the data miner's activities. However, in many domains, including spam detection, intrusion detection, fraud detection, surveillance and counter-terrorism, this is far from the case: the data is actively manipulated by an adversary seeking to make the ...
Nilesh N. Dalvi +4 more
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Applied Sciences, 2023 In this paper, we propose an advanced method for adversarial training that focuses on leveraging the underlying structure of adversarial perturbation distributions. Unlike conventional adversarial training techniques that consider adversarial examples in
Bader Rasheed +2 more
doaj +1 more source
Improving the Robustness of Neural Networks Using K-Support Norm Based Adversarial Training
IEEE Access, 2016 It is of significant importance for any classification and recognition system, which claims near or better than human performance to be immune to small perturbations in the dataset.
Sheikh Waqas Akhtar +6 more
doaj +1 more source
Recent Advances in Adversarial Training for Adversarial Robustness [PDF]
Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, 2021 Adversarial training is one of the most effective approaches for deep learning models to defend against adversarial examples. Unlike other defense strategies, adversarial training aims to enhance the robustness of models intrinsically. During the past few years, adversarial training has been studied and discussed from various aspects, which deserves ...
Tao Bai +4 more
openaire +3 more sources