Authors

* External authors

Venue

Date

Share

Beyond Model Extraction: Imitation Attack for Black-Box NLP APIs

Qiongkai Xu*

Xuanli He*

Lingjuan Lyu

Lizhen Qu*

Gholamreza Haffari*

* External authors

COLING

2022

Abstract

Machine-learning-as-a-service (MLaaS) has attracted millions of users to their splendid large-scale models. Although published as black-box APIs, the valuable models behind these services are still vulnerable to imitation attacks. Recently, a series of works have demonstrated that attackers manage to steal or extract the victim models. Nonetheless, none of the previous stolen models can outperform the original black-box APIs. In this work, we conduct unsupervised domain adaptation and multi-victim ensemble to showing that attackers could potentially surpass victims, which is beyond previous understanding of model extraction. Extensive experiments on both benchmark datasets and real-world APIs validate that the imitators can succeed in outperforming the original black-box models on transferred domains. We consider our work as a milestone in the research of imitation attack, especially on NLP APIs, as the superior performance could influence the defense or even publishing strategy of API providers.

Related Publications

MocoSFL: enabling cross-client collaborative self-supervised learning

ICLR, 2023
Jingtao Li, Lingjuan Lyu, Daisuke Iso, Chaitali Chakrabarti*, Michael Spranger

Existing collaborative self-supervised learning (SSL) schemes are not suitable for cross-client applications because of their expensive computation and large local data requirements. To address these issues, we propose MocoSFL, a collaborative SSL framework based on Split Fe…

IDEAL: Query-Efficient Data-Free Learning from Black-Box Models

ICLR, 2023
Jie Zhang, Chen Chen, Lingjuan Lyu

Knowledge Distillation (KD) is a typical method for training a lightweight student model with the help of a well-trained teacher model. However, most KD methods require access to either the teacher's training data or model parameter, which is unrealistic. To tackle this prob…

Twofer: Tackling Continual Domain Shift with Simultaneous Domain Generalization and Adaptation

ICLR, 2023
Chenxi Liu*, Lixu Wang, Lingjuan Lyu, Chen Sun*, Xiao Wang*, Qi Zhu*

In real-world applications, deep learning models often run in non-stationary environments where the target data distribution continually shifts over time. There have been numerous domain adaptation (DA) methods in both online and offline modes to improve cross-domain adaptat…

  • HOME
  • Publications
  • Beyond Model Extraction: Imitation Attack for Black-Box NLP APIs

JOIN US

Shape the Future of AI with Sony AI

We want to hear from those of you who have a strong desire
to shape the future of AI.