DAC-LoRA: Dynamic Adversarial Curriculum for Efficient and Robust Few-Shot Adaptation

• URL: http://arxiv.org/abs/2509.20792v1
• Date: Thu, 25 Sep 2025 06:20:56 GMT
• Title: DAC-LoRA: Dynamic Adversarial Curriculum for Efficient and Robust Few-Shot Adaptation
• Authors: Ved Umrajkar,
• Abstract summary: We propose Dynamic Adrial Curriculum DAC-LoRA, a novel framework that integrates adversarial training into PEFT.<n>Our work presents an effective, lightweight, and broadly applicable method to demonstrate that the DAC-LoRA framework can be easily integrated into a standard PEFT pipeline to significantly enhance robustness.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Vision-Language Models (VLMs) are foundational to critical applications like autonomous driving, medical diagnosis, and content moderation. While Parameter-Efficient Fine-Tuning (PEFT) methods like LoRA enable their efficient adaptation to specialized tasks, these models remain vulnerable to adversarial attacks that can compromise safety-critical decisions. CLIP, the backbone for numerous downstream VLMs, is a high-value target whose vulnerabilities can cascade across the multimodal AI ecosystem. We propose Dynamic Adversarial Curriculum DAC-LoRA, a novel framework that integrates adversarial training into PEFT. The core principle of our method i.e. an intelligent curriculum of progressively challenging attack, is general and can potentially be applied to any iterative attack method. Guided by the First-Order Stationary Condition (FOSC) and a TRADES-inspired loss, DAC-LoRA achieves substantial improvements in adversarial robustness without significantly compromising clean accuracy. Our work presents an effective, lightweight, and broadly applicable method to demonstrate that the DAC-LoRA framework can be easily integrated into a standard PEFT pipeline to significantly enhance robustness.

Related papers

• RISER: Orchestrating Latent Reasoning Skills for Adaptive Activation Steering [62.63376387138257]
  We propose a plug-and-play intervention framework that adaptively steers large language models (LLMs) reasoning in activation space.<n>RISER constructs a library of reusable reasoning vectors and employs a lightweight Router to dynamically compose them for each input.<n>The Router is optimized via reinforcement learning under task-level rewards, activating latent cognitive primitives in an emergent and compositional manner.
  arXiv  Detail & Related papers  (2026-01-14T08:04:33Z)
• SEQR: Secure and Efficient QR-based LoRA Routing [53.52716967527183]
  Low-Rank Adaptation (LoRA) has become a standard technique for parameter-efficient fine-tuning of large language models.<n> Efficiently selecting the correct LoRA adapter for a given input remains a challenge.<n>We introduce SEQR, an unsupervised LoRA routing algorithm designed to maximize efficiency while providing strict routing guarantees.
  arXiv  Detail & Related papers  (2025-09-22T17:59:38Z)
• PRISM: Robust VLM Alignment with Principled Reasoning for Integrated Safety in Multimodality [41.04710068888387]
  PRISM (Principled Reasoning for Integrated Safety in Multimodality) is a system2-like framework that aligns vision-language models (VLMs)<n>Our framework consists of two key components: PRISM-CoT, a dataset that teaches safety-aware chain-of-thought reasoning, and PRISM-DPO, generated via Monte Carlo Tree Search (MCTS)<n> Comprehensive evaluations demonstrate PRISM's effectiveness, achieving remarkably low attack success rates including 0.15% on JailbreakV-28K for Qwen2-VL and 90% improvement over the previous best method on VLBreak for LLaVA-1.5.
  arXiv  Detail & Related papers  (2025-08-26T03:45:19Z)
• FedStrategist: A Meta-Learning Framework for Adaptive and Robust Aggregation in Federated Learning [0.10241134756773229]
  Federated Learning (FL) offers a paradigm for privacy-preserving collaborative AI, but its decentralized nature creates significant vulnerabilities to model poisoning attacks.<n>This paper introduces FedStrategist, a novel meta-learning framework that reframes robust aggregation as a real-time, cost-aware control problem.
  arXiv  Detail & Related papers  (2025-07-18T18:53:26Z)
• Few-Shot Adversarial Low-Rank Fine-Tuning of Vision-Language Models [13.754960315253014]
  Adrial training is the most effective strategy for improving model robustness in PEFT.<n>We propose AdvCLIP-LoRA, the first algorithm designed to enhance adversarial CLIP models fine-tuned with LoRA in few settings.
  arXiv  Detail & Related papers  (2025-05-21T05:35:24Z)
• C-LoRA: Continual Low-Rank Adaptation for Pre-trained Models [26.560293264523903]
  Low-Rank Adaptation (LoRA) is an efficient fine-tuning method that has been extensively applied in areas such as natural language processing and computer vision.<n>We propose Continual Low-Rank Adaptation (C-LoRA), a novel extension of LoRA for continual learning.<n>C-LoRA uses a learnable routing matrix to dynamically manage parameter updates across tasks.
  arXiv  Detail & Related papers  (2025-02-25T07:35:36Z)
• Adversarial Robustness through Dynamic Ensemble Learning [0.0]
  Adversarial attacks pose a significant threat to the reliability of pre-trained language models (PLMs)<n>This paper presents Adversarial Robustness through Dynamic Ensemble Learning (ARDEL), a novel scheme designed to enhance the robustness of PLMs against such attacks.
  arXiv  Detail & Related papers  (2024-12-20T05:36:19Z)
• Efficient Adversarial Training in LLMs with Continuous Attacks [99.5882845458567]
  Large language models (LLMs) are vulnerable to adversarial attacks that can bypass their safety guardrails. We propose a fast adversarial training algorithm (C-AdvUL) composed of two losses. C-AdvIPO is an adversarial variant of IPO that does not require utility data for adversarially robust alignment.
  arXiv  Detail & Related papers  (2024-05-24T14:20:09Z)
• Policy Smoothing for Provably Robust Reinforcement Learning [109.90239627115336]
  We study the provable robustness of reinforcement learning against norm-bounded adversarial perturbations of the inputs. We generate certificates that guarantee that the total reward obtained by the smoothed policy will not fall below a certain threshold under a norm-bounded adversarial of perturbation the input.
  arXiv  Detail & Related papers  (2021-06-21T21:42:08Z)
• On Fast Adversarial Robustness Adaptation in Model-Agnostic Meta-Learning [100.14809391594109]
  Model-agnostic meta-learning (MAML) has emerged as one of the most successful meta-learning techniques in few-shot learning. Despite the generalization power of the meta-model, it remains elusive that how adversarial robustness can be maintained by MAML in few-shot learning. We propose a general but easily-optimized robustness-regularized meta-learning framework, which allows the use of unlabeled data augmentation, fast adversarial attack generation, and computationally-light fine-tuning.
  arXiv  Detail & Related papers  (2021-02-20T22:03:04Z)
• 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.