Hyper Adversarial Tuning for Boosting Adversarial Robustness of Pretrained Large Vision Models

• URL: http://arxiv.org/abs/2410.05951v1
• Date: Tue, 8 Oct 2024 12:05:01 GMT
• Title: Hyper Adversarial Tuning for Boosting Adversarial Robustness of Pretrained Large Vision Models
• Authors: Kangtao Lv, Huangsen Cao, Kainan Tu, Yihuai Xu, Zhimeng Zhang, Xin Ding, Yongwei Wang,
• Abstract summary: Large vision models have been found vulnerable to adversarial examples, emphasizing the need for enhancing their adversarial robustness. Recent approaches propose robust fine-tuning methods, such as adversarial tuning of low-rank adaptation (LoRA) in large vision models, but they still struggle to match the accuracy of full parameter adversarial fine-tuning. We propose hyper adversarial tuning (HyperAT), which leverages shared defensive knowledge among different methods to improve model robustness efficiently and effectively simultaneously.
• Score: 9.762046320216005
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large vision models have been found vulnerable to adversarial examples, emphasizing the need for enhancing their adversarial robustness. While adversarial training is an effective defense for deep convolutional models, it often faces scalability issues with large vision models due to high computational costs. Recent approaches propose robust fine-tuning methods, such as adversarial tuning of low-rank adaptation (LoRA) in large vision models, but they still struggle to match the accuracy of full parameter adversarial fine-tuning. The integration of various defense mechanisms offers a promising approach to enhancing the robustness of large vision models, yet this paradigm remains underexplored. To address this, we propose hyper adversarial tuning (HyperAT), which leverages shared defensive knowledge among different methods to improve model robustness efficiently and effectively simultaneously. Specifically, adversarial tuning of each defense method is formulated as a learning task, and a hypernetwork generates LoRA specific to this defense. Then, a random sampling and tuning strategy is proposed to extract and facilitate the defensive knowledge transfer between different defenses. Finally, diverse LoRAs are merged to enhance the adversarial robustness. Experiments on various datasets and model architectures demonstrate that HyperAT significantly enhances the adversarial robustness of pretrained large vision models without excessive computational overhead, establishing a new state-of-the-art benchmark.

Related papers

• Protecting Feed-Forward Networks from Adversarial Attacks Using Predictive Coding [0.20718016474717196]
  An adversarial example is a modified input image designed to cause a Machine Learning (ML) model to make a mistake. This study presents a practical and effective solution -- using predictive coding networks (PCnets) as an auxiliary step for adversarial defence.
  arXiv  Detail & Related papers  (2024-10-31T21:38:05Z)
• A Hybrid Defense Strategy for Boosting Adversarial Robustness in Vision-Language Models [9.304845676825584]
  We propose a novel adversarial training framework that integrates multiple attack strategies and advanced machine learning techniques. Experiments conducted on real-world datasets, including CIFAR-10 and CIFAR-100, demonstrate that the proposed method significantly enhances model robustness.
  arXiv  Detail & Related papers  (2024-10-18T23:47:46Z)
• Meta Invariance Defense Towards Generalizable Robustness to Unknown Adversarial Attacks [62.036798488144306]
  Current defense mainly focuses on the known attacks, but the adversarial robustness to the unknown attacks is seriously overlooked. We propose an attack-agnostic defense method named Meta Invariance Defense (MID) We show that MID simultaneously achieves robustness to the imperceptible adversarial perturbations in high-level image classification and attack-suppression in low-level robust image regeneration.
  arXiv  Detail & Related papers  (2024-04-04T10:10:38Z)
• FullLoRA-AT: Efficiently Boosting the Robustness of Pretrained Vision Transformers [61.48709409150777]
  Vision Transformer (ViT) model has gradually become mainstream in various computer vision tasks. Existing large models tend to prioritize performance during training, potentially neglecting the robustness. We develop a novel LNLoRA module, incorporating a learnable layer normalization before the conventional LoRA module. We propose the FullLoRA-AT framework by integrating the learnable LNLoRA modules into all key components of ViT-based models.
  arXiv  Detail & Related papers  (2024-01-03T14:08:39Z)
• Parameter-Saving Adversarial Training: Reinforcing Multi-Perturbation Robustness via Hypernetworks [47.21491911505409]
  Adrial training serves as one of the most popular and effective methods to defend against adversarial perturbations. We propose a novel multi-perturbation adversarial training framework, parameter-saving adversarial training (PSAT), to reinforce multi-perturbation robustness.
  arXiv  Detail & Related papers  (2023-09-28T07:16:02Z)
• A Multi-objective Memetic Algorithm for Auto Adversarial Attack Optimization Design [1.9100854225243937]
  Well-designed adversarial defense strategies can improve the robustness of deep learning models against adversarial examples. Given the defensed model, the efficient adversarial attack with less computational burden and lower robust accuracy is needed to be further exploited. We propose a multi-objective memetic algorithm for auto adversarial attack optimization design, which realizes the automatical search for the near-optimal adversarial attack towards defensed models.
  arXiv  Detail & Related papers  (2022-08-15T03:03:05Z)
• Model-Agnostic Meta-Attack: Towards Reliable Evaluation of Adversarial Robustness [53.094682754683255]
  We propose a Model-Agnostic Meta-Attack (MAMA) approach to discover stronger attack algorithms automatically. Our method learns the in adversarial attacks parameterized by a recurrent neural network. We develop a model-agnostic training algorithm to improve the ability of the learned when attacking unseen defenses.
  arXiv  Detail & Related papers  (2021-10-13T13:54:24Z)
• Adaptive Feature Alignment for Adversarial Training [56.17654691470554]
  CNNs are typically vulnerable to adversarial attacks, which pose a threat to security-sensitive applications. We propose the adaptive feature alignment (AFA) to generate features of arbitrary attacking strengths. Our method is trained to automatically align features of arbitrary attacking strength.
  arXiv  Detail & Related papers  (2021-05-31T17:01:05Z)
• Boosting Adversarial Training with Hypersphere Embedding [53.75693100495097]
  Adversarial training is one of the most effective defenses against adversarial attacks for deep learning models. In this work, we advocate incorporating the hypersphere embedding mechanism into the AT procedure. We validate our methods under a wide range of adversarial attacks on the CIFAR-10 and ImageNet datasets.
  arXiv  Detail & Related papers  (2020-02-20T08:42:29Z)

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.