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Apruzzese, Giovanni (author), Conti, M. (author), Yuan, Ying (author)
Existing literature on adversarial Machine Learning (ML) focuses either on showing attacks that break every ML model, or defenses that withstand most attacks. Unfortunately, little consideration is given to the actual cost of the attack or the defense. Moreover, adversarial samples are often crafted in the "feature-space", making the...
conference paper 2022