merrafelice/Assessing-Perceptual-and-Recommendation-Mutation-of-Adversarially-Poisoned-Visual-Recommenders

In this work, we provide 24 combinations of attack/defense strategies, and visual-based recommenders to 1) access performance alteration on recommendation and 2) empirically verify the effect on final users through offline visual metrics.

Python