  Robust Fine-Tuning (RFT) is a low-cost strategy to obtain adversarial
robustness in downstream applications, without requiring a lot of computational
resources and collecting significant amounts of data. This paper uncovers an
issue with the existing RFT, where optimizing both adversarial and natural
objectives through the feature extractor (FE) yields significantly divergent
gradient directions. This divergence introduces instability in the optimization
process, thereby hindering the attainment of adversarial robustness and
rendering RFT highly sensitive to hyperparameters. To mitigate this issue, we
propose a low-rank (LoRa) branch that disentangles RFT into two distinct
components: optimizing natural objectives via the LoRa branch and adversarial
objectives via the FE. Besides, we introduce heuristic strategies for
automating the scheduling of the learning rate and the scalars of loss terms.
Extensive empirical evaluations demonstrate that our proposed automated RFT
disentangled via the LoRa branch (AutoLoRa) achieves new state-of-the-art
results across a range of downstream tasks. AutoLoRa holds significant
practical utility, as it automatically converts a pre-trained FE into an
adversarially robust model for downstream tasks without the need for searching
hyperparameters.

Machine Learning;Cryptography and Security