Flew Over Learning Trap: Learn Unlearnable Samples by Progressive Staged Training

• URL: http://arxiv.org/abs/2306.02064v1
• Date: Sat, 3 Jun 2023 09:36:16 GMT
• Title: Flew Over Learning Trap: Learn Unlearnable Samples by Progressive Staged Training
• Authors: Pucheng Dang, Xing Hu, Kaidi Xu, Jinhao Duan, Di Huang, Husheng Han, Rui Zhang, Zidong Du, Qi Guo, Yunji Chen
• Abstract summary: Unlearning techniques generate unlearnable samples by adding imperceptible perturbations to data for public publishing. We make the in-depth analysis and observe that models can learn both image features and perturbation features of unlearnable samples at an early stage. We propose Progressive Staged Training to effectively prevent models from overfitting in learning perturbation features.
• Score: 28.17601195439716
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Unlearning techniques are proposed to prevent third parties from exploiting unauthorized data, which generate unlearnable samples by adding imperceptible perturbations to data for public publishing. These unlearnable samples effectively misguide model training to learn perturbation features but ignore image semantic features. We make the in-depth analysis and observe that models can learn both image features and perturbation features of unlearnable samples at an early stage, but rapidly go to the overfitting stage since the shallow layers tend to overfit on perturbation features and make models fall into overfitting quickly. Based on the observations, we propose Progressive Staged Training to effectively prevent models from overfitting in learning perturbation features. We evaluated our method on multiple model architectures over diverse datasets, e.g., CIFAR-10, CIFAR-100, and ImageNet-mini. Our method circumvents the unlearnability of all state-of-the-art methods in the literature and provides a reliable baseline for further evaluation of unlearnable techniques.

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