Generating Adversarial Examples through Latent Space Exploration of Generative Adversarial Networks-Reference-Cited by-同舟云学术

Generating Adversarial Examples through Latent Space Exploration of Generative Adversarial Networks

Published:2023-07-15 Issue: Volume: Page:
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Container-title:Proceedings of the Companion Conference on Genetic and Evolutionary Computation
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Author:

Clare Luana¹^ORCID,Correia João¹^ORCID

Affiliation:

1. University of Coimbra, CISUC, DEI, Coimbra, Portugal

Funder

Agenda Mobilizadora Sines Nexus. ref. No. 7113, supported by the Recovery and Resilience Plan (PRR) and by the European Funds Next Generation EU, following Notice No. 02/C05-i01/2022, Component 5 - Capitalization and Business Innovation - Mobilizing Agendas for Business Innovation

FCT - Foundation for Science and Technology, I.P./MCTES through national funds (PIDDAC), within the scope of CISUC R\&D Unit -

FCT - Foundation for Science and Technology, I.P./MCTES through national funds (PIDDAC), within the scope of CISUC R\&D Unit - project code

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3583133.3596392

Reference18 articles.

1. DeepMasterPrints: Generating MasterPrints for Dictionary Attacks via Latent Variable Evolution*

2. Nicholas Carlini and David A . Wagner . 2016 . Towards Evaluating the Robustness of Neural Networks. CoRR abs/1608.04644 (2016). arXiv:1608.04644 http://arxiv.org/abs/1608.04644 Nicholas Carlini and David A. Wagner. 2016. Towards Evaluating the Robustness of Neural Networks. CoRR abs/1608.04644 (2016). arXiv:1608.04644 http://arxiv.org/abs/1608.04644

3. POBA-GA: Perturbation optimized black-box adversarial attacks via genetic algorithm

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