  The unprecedented availability of training data fueled the rapid development
of powerful neural networks in recent years. However, the need for such large
amounts of data leads to potential threats such as poisoning attacks:
adversarial manipulations of the training data aimed at compromising the
learned model to achieve a given adversarial goal.
  This paper investigates defenses against clean-label poisoning attacks and
proposes a novel approach to detect and filter poisoned datapoints in the
transfer learning setting. We define a new characteristic vector representation
of datapoints and show that it effectively captures the intrinsic properties of
the data distribution. Through experimental analysis, we demonstrate that
effective poisons can be successfully differentiated from clean points in the
characteristic vector space. We thoroughly evaluate our proposed approach and
compare it to existing state-of-the-art defenses using multiple architectures,
datasets, and poison budgets. Our evaluation shows that our proposal
outperforms existing approaches in defense rate and final trained model
performance across all experimental settings.
