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Summary of Adversarial Attacks on Hidden Tasks in Multi-task Learning, by Yu Zhe et al.


Adversarial Attacks on Hidden Tasks in Multi-Task Learning

by Yu Zhe, Rei Nagaike, Daiki Nishiyama, Kazuto Fukuchi, Jun Sakuma

First submitted to arxiv on: 24 May 2024

Categories

  • Main: Machine Learning (cs.LG)
  • Secondary: None

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Summary difficulty Written by Summary
High Paper authors High Difficulty Summary
Read the original abstract here
Medium GrooveSquid.com (original content) Medium Difficulty Summary
This paper investigates the vulnerability of multi-task learning models to targeted attacks. Specifically, it explores the ability to compromise a model’s knowledge of a “hidden” task by leveraging information from other, related tasks and the shared network architecture. The proposed attack method, which exploits the model’s reliance on common representations across tasks, successfully degrades the accuracy of the target task while preserving performance on visible tasks.
Low GrooveSquid.com (original content) Low Difficulty Summary
The researchers tested their method on two datasets: CelebA and DeepFashion. They found that their approach can effectively reduce the model’s ability to classify images in the hidden task, even when only non-target task information is available. This study contributes to our understanding of adversarial vulnerabilities in multi-task classifiers.

Keywords

» Artificial intelligence  » Multi task