Renofeation: A Simple Transfer Learning Method for Improved Adversarial Robustness
This addresses security concerns for industrial applications using transfer learning, offering a more robust alternative to fine-tuning.
The paper tackles the vulnerability of fine-tuned models to adversarial attacks in transfer learning, proposing a new method that achieves competitive clean-data performance while improving robustness.
Fine-tuning through knowledge transfer from a pre-trained model on a large-scale dataset is a widely spread approach to effectively build models on small-scale datasets. In this work, we show that a recent adversarial attack designed for transfer learning via re-training the last linear layer can successfully deceive models trained with transfer learning via end-to-end fine-tuning. This raises security concerns for many industrial applications. In contrast, models trained with random initialization without transfer are much more robust to such attacks, although these models often exhibit much lower accuracy. To this end, we propose noisy feature distillation, a new transfer learning method that trains a network from random initialization while achieving clean-data performance competitive with fine-tuning. Code available at https://github.com/cmu-enyac/Renofeation.