Investigate the Suitability of Adversarial Perturbation in Preserving Privacy in the Context of Photos
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-47448-4_31
Reference13 articles.
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3. Fawzi, A., Fawzi, O., Frossard, P.: Analysis of classifiers’ robustness to adversarial perturbations. Mach. Learn. 107(3), 481–508 (2018). https://doi.org/10.1007/s10994-017-5663-3
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5. Khalid, F., Hanif, M.A., Rehman, S., Qadir, J., Shafique, M.: FAdeML: Understanding the impact of pre-processing noise filtering on adversarial machine learning. In: 2019 Design, Automation & Test in Europe Conference & Exhibition (DATE), pp. 902–907 (2019). https://doi.org/10.23919/DATE.2019.8715141
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