Improving Gradient-based Adversarial Training for Text Classification by Contrastive Learning and Auto-Encoder

• URL: http://arxiv.org/abs/2109.06536v1
• Date: Tue, 14 Sep 2021 09:08:58 GMT
• Title: Improving Gradient-based Adversarial Training for Text Classification by Contrastive Learning and Auto-Encoder
• Authors: Yao Qiu, Jinchao Zhang, Jie Zhou
• Abstract summary: We focus on enhancing the model's ability to defend gradient-based adversarial attack during the model's training process. We propose two novel adversarial training approaches: CARL and RAR. Experiments show that the proposed two approaches outperform strong baselines on various text classification datasets.
• Score: 18.375585982984845
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent work has proposed several efficient approaches for generating gradient-based adversarial perturbations on embeddings and proved that the model's performance and robustness can be improved when they are trained with these contaminated embeddings. While they paid little attention to how to help the model to learn these adversarial samples more efficiently. In this work, we focus on enhancing the model's ability to defend gradient-based adversarial attack during the model's training process and propose two novel adversarial training approaches: (1) CARL narrows the original sample and its adversarial sample in the representation space while enlarging their distance from different labeled samples. (2) RAR forces the model to reconstruct the original sample from its adversarial representation. Experiments show that the proposed two approaches outperform strong baselines on various text classification datasets. Analysis experiments find that when using our approaches, the semantic representation of the input sentence won't be significantly affected by adversarial perturbations, and the model's performance drops less under adversarial attack. That is to say, our approaches can effectively improve the robustness of the model. Besides, RAR can also be used to generate text-form adversarial samples.

Related papers

• MirrorCheck: Efficient Adversarial Defense for Vision-Language Models [55.73581212134293]
  We propose a novel, yet elegantly simple approach for detecting adversarial samples in Vision-Language Models. Our method leverages Text-to-Image (T2I) models to generate images based on captions produced by target VLMs. Empirical evaluations conducted on different datasets validate the efficacy of our approach.
  arXiv  Detail & Related papers  (2024-06-13T15:55:04Z)
• READ: Improving Relation Extraction from an ADversarial Perspective [33.44949503459933]
  We propose an adversarial training method specifically designed for relation extraction (RE) Our approach introduces both sequence- and token-level perturbations to the sample and uses a separate perturbation vocabulary to improve the search for entity and context perturbations.
  arXiv  Detail & Related papers  (2024-04-02T16:42:44Z)
• SA-Attack: Improving Adversarial Transferability of Vision-Language Pre-training Models via Self-Augmentation [56.622250514119294]
  In contrast to white-box adversarial attacks, transfer attacks are more reflective of real-world scenarios. We propose a self-augment-based transfer attack method, termed SA-Attack.
  arXiv  Detail & Related papers  (2023-12-08T09:08:50Z)
• Advancing Adversarial Robustness Through Adversarial Logit Update [10.041289551532804]
  Adversarial training and adversarial purification are among the most widely recognized defense strategies. We propose a new principle, namely Adversarial Logit Update (ALU), to infer adversarial sample's labels. Our solution achieves superior performance compared to state-of-the-art methods against a wide range of adversarial attacks.
  arXiv  Detail & Related papers  (2023-08-29T07:13:31Z)
• AdvDiff: Generating Unrestricted Adversarial Examples using Diffusion Models [7.406040859734522]
  Unrestricted adversarial attacks present a serious threat to deep learning models and adversarial defense techniques. Previous attack methods often directly inject Projected Gradient Descent (PGD) gradients into the sampling of generative models. We propose a new method, called AdvDiff, to generate unrestricted adversarial examples with diffusion models.
  arXiv  Detail & Related papers  (2023-07-24T03:10:02Z)
• Improving Adversarial Robustness to Sensitivity and Invariance Attacks with Deep Metric Learning [80.21709045433096]
  A standard method in adversarial robustness assumes a framework to defend against samples crafted by minimally perturbing a sample. We use metric learning to frame adversarial regularization as an optimal transport problem. Our preliminary results indicate that regularizing over invariant perturbations in our framework improves both invariant and sensitivity defense.
  arXiv  Detail & Related papers  (2022-11-04T13:54:02Z)
• The Enemy of My Enemy is My Friend: Exploring Inverse Adversaries for Improving Adversarial Training [72.39526433794707]
  Adversarial training and its variants have been shown to be the most effective approaches to defend against adversarial examples. We propose a novel adversarial training scheme that encourages the model to produce similar outputs for an adversarial example and its inverse adversarial'' counterpart. Our training method achieves state-of-the-art robustness as well as natural accuracy.
  arXiv  Detail & Related papers  (2022-11-01T15:24:26Z)
• Resisting Adversarial Attacks in Deep Neural Networks using Diverse Decision Boundaries [12.312877365123267]
  Deep learning systems are vulnerable to crafted adversarial examples, which may be imperceptible to the human eye, but can lead the model to misclassify. We develop a new ensemble-based solution that constructs defender models with diverse decision boundaries with respect to the original model. We present extensive experimentations using standard image classification datasets, namely MNIST, CIFAR-10 and CIFAR-100 against state-of-the-art adversarial attacks.
  arXiv  Detail & Related papers  (2022-08-18T08:19:26Z)
• 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)
• Query-Free Adversarial Transfer via Undertrained Surrogates [14.112444998191698]
  We introduce a new method for improving the efficacy of adversarial attacks in a black-box setting by undertraining the surrogate model which the attacks are generated on. We show that this method transfers well across architectures and outperforms state-of-the-art methods by a wide margin.
  arXiv  Detail & Related papers  (2020-07-01T23:12:22Z)
• Adversarial Distributional Training for Robust Deep Learning [53.300984501078126]
  Adversarial training (AT) is among the most effective techniques to improve model robustness by augmenting training data with adversarial examples. Most existing AT methods adopt a specific attack to craft adversarial examples, leading to the unreliable robustness against other unseen attacks. In this paper, we introduce adversarial distributional training (ADT), a novel framework for learning robust models.
  arXiv  Detail & Related papers  (2020-02-14T12:36:59Z)

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.