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

Finding a needle in a haystack: A Black-Box Approach to Invisible Watermark Detection

ECCV, 2024
Minzhou Pan*, Zhenting Wang, Xin Dong, Vikash Sehwag, Lingjuan Lyu, Xue Lin*

In this paper, we propose WaterMark Detection (WMD), the first invisible watermark detection method under a black-box and annotation-free setting. WMD is capable of detecting arbitrary watermarks within a given reference dataset using a clean non watermarked dataset as a ref…

PerceptAnon: Exploring the Human Perception of Image Anonymization Beyond Pseudonymization for GDPR

ICML, 2024
Kartik Patwari, Chen-Nee Chuah*, Lingjuan Lyu, Vivek Sharma

Current image anonymization techniques, largely focus on localized pseudonymization, typically modify identifiable features like faces or full bodies and evaluate anonymity through metrics such as detection and re-identification rates. However, this approach often overlooks …

COALA: A Practical and Vision-Centric Federated Learning Platform

ICML, 2024
Weiming Zhuang, Jian Xu, Chen Chen, Jingtao Li, Lingjuan Lyu

We present COALA, a vision-centric Federated Learning (FL) platform, and a suite of benchmarks for practical FL scenarios, which we categorize as task, data, and model levels. At the task level, COALA extends support from simple classification to 15 computer vision tasks, in…

  • 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.