How to Enhance Downstream Adversarial Robustness (almost) without Touching the Pre-Trained Foundation Model?

• URL: http://arxiv.org/abs/2504.10850v1
• Date: Tue, 15 Apr 2025 04:17:37 GMT
• Title: How to Enhance Downstream Adversarial Robustness (almost) without Touching the Pre-Trained Foundation Model?
• Authors: Meiqi Liu, Zhuoqun Huang, Yue Xing,
• Abstract summary: We propose a robust auto-encoder as a data pre-processing method before feeding the data into the foundation model.<n>The proposed approach has zero access to the foundation model when training the robust auto-encoder.
• Score: 6.901549479897846
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: With the rise of powerful foundation models, a pre-training-fine-tuning paradigm becomes increasingly popular these days: A foundation model is pre-trained using a huge amount of data from various sources, and then the downstream users only need to fine-tune and adapt it to specific downstream tasks. However, due to the high computation complexity of adversarial training, it is not feasible to fine-tune the foundation model to improve its robustness on the downstream task. Observing the above challenge, we want to improve the downstream robustness without updating/accessing the weights in the foundation model. Inspired from existing literature in robustness inheritance (Kim et al., 2020), through theoretical investigation, we identify a close relationship between robust contrastive learning with the adversarial robustness of supervised learning. To further validate and utilize this theoretical insight, we design a simple-yet-effective robust auto-encoder as a data pre-processing method before feeding the data into the foundation model. The proposed approach has zero access to the foundation model when training the robust auto-encoder. Extensive experiments demonstrate the effectiveness of the proposed method in improving the robustness of downstream tasks, verifying the connection between the feature robustness (implied by small adversarial contrastive loss) and the robustness of the downstream task.

Related papers

• Adversarial Robustification via Text-to-Image Diffusion Models [56.37291240867549]
  Adrial robustness has been conventionally believed as a challenging property to encode for neural networks. We develop a scalable and model-agnostic solution to achieve adversarial robustness without using any data.
  arXiv  Detail & Related papers  (2024-07-26T10:49:14Z)
• Initialization Matters for Adversarial Transfer Learning [61.89451332757625]
  We discover the necessity of an adversarially robust pretrained model. We propose Robust Linear Initialization (RoLI) for adversarial finetuning, which initializes the linear head with the weights obtained by adversarial linear probing. Across five different image classification datasets, we demonstrate the effectiveness of RoLI and achieve new state-of-the-art results.
  arXiv  Detail & Related papers  (2023-12-10T00:51:05Z)
• Doubly Robust Instance-Reweighted Adversarial Training [107.40683655362285]
  We propose a novel doubly-robust instance reweighted adversarial framework. Our importance weights are obtained by optimizing the KL-divergence regularized loss function. Our proposed approach outperforms related state-of-the-art baseline methods in terms of average robust performance.
  arXiv  Detail & Related papers  (2023-08-01T06:16:18Z)
• Enhancing Multiple Reliability Measures via Nuisance-extended Information Bottleneck [77.37409441129995]
  In practical scenarios where training data is limited, many predictive signals in the data can be rather from some biases in data acquisition. We consider an adversarial threat model under a mutual information constraint to cover a wider class of perturbations in training. We propose an autoencoder-based training to implement the objective, as well as practical encoder designs to facilitate the proposed hybrid discriminative-generative training.
  arXiv  Detail & Related papers  (2023-03-24T16:03:21Z)
• TWINS: A Fine-Tuning Framework for Improved Transferability of Adversarial Robustness and Generalization [89.54947228958494]
  This paper focuses on the fine-tuning of an adversarially pre-trained model in various classification tasks. We propose a novel statistics-based approach, Two-WIng NormliSation (TWINS) fine-tuning framework. TWINS is shown to be effective on a wide range of image classification datasets in terms of both generalization and robustness.
  arXiv  Detail & Related papers  (2023-03-20T14:12:55Z)
• Towards Robust Dataset Learning [90.2590325441068]
  We propose a principled, tri-level optimization to formulate the robust dataset learning problem. Under an abstraction model that characterizes robust vs. non-robust features, the proposed method provably learns a robust dataset.
  arXiv  Detail & Related papers  (2022-11-19T17:06:10Z)
• Deep Ensembles for Low-Data Transfer Learning [21.578470914935938]
  We study different ways of creating ensembles from pre-trained models. We show that the nature of pre-training itself is a performant source of diversity. We propose a practical algorithm that efficiently identifies a subset of pre-trained models for any downstream dataset.
  arXiv  Detail & Related papers  (2020-10-14T07:59:00Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.