Erosion Attack for Adversarial Training to Enhance Semantic Segmentation Robustness
- URL: http://arxiv.org/abs/2601.14950v1
- Date: Wed, 21 Jan 2026 12:52:09 GMT
- Title: Erosion Attack for Adversarial Training to Enhance Semantic Segmentation Robustness
- Authors: Yufei Song, Ziqi Zhou, Menghao Deng, Yifan Hu, Shengshan Hu, Minghui Li, Leo Yu Zhang,
- Abstract summary: We propose EroSeg-AT, a vulnerability-aware adversarial training framework that leverages EroSeg to generate adversarial examples.<n>EroSeg first selects sensitive pixels based on pixel-level confidence and then progressively propagates perturbations to higher-confidence pixels, effectively disrupting the semantic consistency of the samples.
- Score: 43.63509019035562
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Existing segmentation models exhibit significant vulnerability to adversarial attacks.To improve robustness, adversarial training incorporates adversarial examples into model training. However, existing attack methods consider only global semantic information and ignore contextual semantic relationships within the samples, limiting the effectiveness of adversarial training. To address this issue, we propose EroSeg-AT, a vulnerability-aware adversarial training framework that leverages EroSeg to generate adversarial examples. EroSeg first selects sensitive pixels based on pixel-level confidence and then progressively propagates perturbations to higher-confidence pixels, effectively disrupting the semantic consistency of the samples. Experimental results show that, compared to existing methods, our approach significantly improves attack effectiveness and enhances model robustness under adversarial training.
Related papers
- Adversarial Defence without Adversarial Defence: Enhancing Language Model Robustness via Instance-level Principal Component Removal [28.588188876688037]
Pre-trained language models (PLMs) have driven substantial progress in natural language processing but remain vulnerable to adversarial attacks.<n>We propose a simple yet effective add-on module that enhances the adversarial robustness of PLMs.
arXiv Detail & Related papers (2025-07-29T12:31:26Z) - Mutual-modality Adversarial Attack with Semantic Perturbation [81.66172089175346]
We propose a novel approach that generates adversarial attacks in a mutual-modality optimization scheme.
Our approach outperforms state-of-the-art attack methods and can be readily deployed as a plug-and-play solution.
arXiv Detail & Related papers (2023-12-20T05:06:01Z) - Improved Adversarial Training Through Adaptive Instance-wise Loss
Smoothing [5.1024659285813785]
Adversarial training has been the most successful defense against such adversarial attacks.
We propose a new adversarial training method: Instance-adaptive Smoothness Enhanced Adversarial Training.
Our method achieves state-of-the-art robustness against $ell_infty$-norm constrained attacks.
arXiv Detail & Related papers (2023-03-24T15:41:40Z) - 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) - Improving Adversarial Robustness with Self-Paced Hard-Class Pair
Reweighting [5.084323778393556]
adversarial training with untargeted attacks is one of the most recognized methods.
We find that the naturally imbalanced inter-class semantic similarity makes those hard-class pairs to become the virtual targets of each other.
We propose to upweight hard-class pair loss in model optimization, which prompts learning discriminative features from hard classes.
arXiv Detail & Related papers (2022-10-26T22:51:36Z) - 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) - Improving White-box Robustness of Pre-processing Defenses via Joint Adversarial Training [106.34722726264522]
A range of adversarial defense techniques have been proposed to mitigate the interference of adversarial noise.
Pre-processing methods may suffer from the robustness degradation effect.
A potential cause of this negative effect is that adversarial training examples are static and independent to the pre-processing model.
We propose a method called Joint Adversarial Training based Pre-processing (JATP) defense.
arXiv Detail & Related papers (2021-06-10T01:45:32Z) - Semantics-Preserving Adversarial Training [12.242659601882147]
Adversarial training is a technique that improves adversarial robustness of a deep neural network (DNN) by including adversarial examples in the training data.
We propose semantics-preserving adversarial training (SPAT) which encourages perturbation on the pixels that are shared among all classes.
Experiment results show that SPAT improves adversarial robustness and achieves state-of-the-art results in CIFAR-10 and CIFAR-100.
arXiv Detail & Related papers (2020-09-23T07:42:14Z) - Improving adversarial robustness of deep neural networks by using
semantic information [17.887586209038968]
Adrial training is the main method for improving adversarial robustness and the first line of defense against adversarial attacks.
This paper provides a new perspective on the issue of adversarial robustness, one that shifts the focus from the network as a whole to the critical part of the region close to the decision boundary corresponding to a given class.
Experimental results on the MNIST and CIFAR-10 datasets show that this approach greatly improves adversarial robustness even using a very small dataset from the training data.
arXiv Detail & Related papers (2020-08-18T10:23:57Z) - Stylized Adversarial Defense [105.88250594033053]
adversarial training creates perturbation patterns and includes them in the training set to robustify the model.
We propose to exploit additional information from the feature space to craft stronger adversaries.
Our adversarial training approach demonstrates strong robustness compared to state-of-the-art defenses.
arXiv Detail & Related papers (2020-07-29T08:38:10Z)
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.