As one of the most powerful topic models, Latent Dirichlet Allocation (LDA)
has been used in a vast range of tasks, including document understanding,
information retrieval and peer-reviewer assignment. Despite its tremendous
popularity, the security of LDA has rarely been studied. This poses severe
risks to security-critical tasks such as sentiment analysis and peer-reviewer
assignment that are based on LDA. In this paper, we are interested in knowing
whether LDA models are vulnerable to adversarial perturbations of benign
document examples during inference time. We formalize the evasion attack to LDA
models as an optimization problem and prove it to be NP-hard. We then propose a
novel and efficient algorithm, EvaLDA to solve it. We show the effectiveness of
EvaLDA via extensive empirical evaluations. For instance, in the NIPS dataset,
EvaLDA can averagely promote the rank of a target topic from 10 to around 7 by
only replacing 1% of the words with similar words in a victim document. Our
work provides significant insights into the power and limitations of evasion
attacks to LDA models.

本文研究了 Latent Dirichlet Allocation（LDA）模型在推理期间是否容易受到对抗性扰动的攻击，并将其形式化为一个 NP 难问题。我们提出了一种新颖高效的算法 EvaLDA 来解决这个问题，并通过广泛的实证评估展示了它的有效性。