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Latent-factor models (LFM) based on collaborative filtering (CF), such as matrix factorization (MF) and deep CF methods, are widely used in modern recommender systems (RS) due to their excellent performance and recommendation accuracy. However, success has been accompanied with a major new arising challenge: many applications of machine learning (ML) are adversarial in nature. In recent years, it has been shown that these methods are vulnerable to adversarial examples, i.e., subtle butarXiv:2005.10322v2 fatcat:4wqcluqgnbbwpkicunn42et5te