Dynamic Divide-and-Conquer Adversarial Training for Robust Semantic
Segmentation
- URL: http://arxiv.org/abs/2003.06555v2
- Date: Mon, 16 Aug 2021 15:00:16 GMT
- Title: Dynamic Divide-and-Conquer Adversarial Training for Robust Semantic
Segmentation
- Authors: Xiaogang Xu, Hengshuang Zhao, Jiaya Jia
- Abstract summary: Adversarial training is promising for improving robustness of deep neural networks towards adversarial perturbations.
We formulate a general adversarial training procedure that can perform decently on both adversarial and clean samples.
We propose a dynamic divide-and-conquer adversarial training (DDC-AT) strategy to enhance the defense effect.
- Score: 79.42338812621874
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Adversarial training is promising for improving robustness of deep neural
networks towards adversarial perturbations, especially on the classification
task. The effect of this type of training on semantic segmentation, contrarily,
just commences. We make the initial attempt to explore the defense strategy on
semantic segmentation by formulating a general adversarial training procedure
that can perform decently on both adversarial and clean samples. We propose a
dynamic divide-and-conquer adversarial training (DDC-AT) strategy to enhance
the defense effect, by setting additional branches in the target model during
training, and dealing with pixels with diverse properties towards adversarial
perturbation. Our dynamical division mechanism divides pixels into multiple
branches automatically. Note all these additional branches can be abandoned
during inference and thus leave no extra parameter and computation cost.
Extensive experiments with various segmentation models are conducted on PASCAL
VOC 2012 and Cityscapes datasets, in which DDC-AT yields satisfying performance
under both white- and black-box attack.
Related papers
- MOREL: Enhancing Adversarial Robustness through Multi-Objective Representation Learning [1.534667887016089]
deep neural networks (DNNs) are vulnerable to slight adversarial perturbations.
We show that strong feature representation learning during training can significantly enhance the original model's robustness.
We propose MOREL, a multi-objective feature representation learning approach, encouraging classification models to produce similar features for inputs within the same class, despite perturbations.
arXiv Detail & Related papers (2024-10-02T16:05:03Z) - 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) - SegPGD: An Effective and Efficient Adversarial Attack for Evaluating and
Boosting Segmentation Robustness [63.726895965125145]
Deep neural network-based image classifications are vulnerable to adversarial perturbations.
In this work, we propose an effective and efficient segmentation attack method, dubbed SegPGD.
Since SegPGD can create more effective adversarial examples, the adversarial training with our SegPGD can boost the robustness of segmentation models.
arXiv Detail & Related papers (2022-07-25T17:56:54Z) - Distributed Adversarial Training to Robustify Deep Neural Networks at
Scale [100.19539096465101]
Current deep neural networks (DNNs) are vulnerable to adversarial attacks, where adversarial perturbations to the inputs can change or manipulate classification.
To defend against such attacks, an effective approach, known as adversarial training (AT), has been shown to mitigate robust training.
We propose a large-batch adversarial training framework implemented over multiple machines.
arXiv Detail & Related papers (2022-06-13T15:39:43Z) - Enhancing Adversarial Training with Feature Separability [52.39305978984573]
We introduce a new concept of adversarial training graph (ATG) with which the proposed adversarial training with feature separability (ATFS) enables to boost the intra-class feature similarity and increase inter-class feature variance.
Through comprehensive experiments, we demonstrate that the proposed ATFS framework significantly improves both clean and robust performance.
arXiv Detail & Related papers (2022-05-02T04:04:23Z) - Ada-Segment: Automated Multi-loss Adaptation for Panoptic Segmentation [95.31590177308482]
We propose an automated multi-loss adaptation (named Ada-Segment) to flexibly adjust multiple training losses over the course of training.
With an end-to-end architecture, Ada-Segment generalizes to different datasets without the need of re-tuning hyper parameters.
Ada-Segment brings 2.7% panoptic quality (PQ) improvement on COCO val split from the vanilla baseline, achieving the state-of-the-art 48.5% PQ on COCO test-dev split and 32.9% PQ on ADE20K dataset.
arXiv Detail & Related papers (2020-12-07T11:43:10Z) - 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) - Improved Noise and Attack Robustness for Semantic Segmentation by Using
Multi-Task Training with Self-Supervised Depth Estimation [39.99513327031499]
We propose to improve robustness by a multi-task training, which extends supervised semantic segmentation by a self-supervised monocular depth estimation on unlabeled videos.
We show the effectiveness of our method on the Cityscapes dataset, where our multi-task training approach consistently outperforms the single-task semantic segmentation baseline.
arXiv Detail & Related papers (2020-04-23T11:03:56Z)
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.