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Better safe than sorry: preventing delusive adversaries with adversarial training 21

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Better safe than sorry: preventing delusive adversaries with...

Proceedings of the 35th International Conference on Neural Information Processing Systems

作者： Lue Tao Lei Feng Jinfeng Yi Sheng-Jun Huang Songcan Chen College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics and MIIT Key Laboratory of Pattern Analysis and Machine Intelligence College of Computer Science Chongqing University JD AI Research

ISBN: (纸本)9781713845393

Delusive attacks aim to substantially deteriorate the test accuracy of the learning model by slightly perturbing the features of correctly labeled training examples. By formalizing this malicious attack as finding the worst-case training data within a specific ∞-Wasserstein ball, we show that minimizing adversarial risk on the perturbed data is equivalent to optimizing an upper bound of natural risk on the original data. This implies that adversarial training can serve as a principled defense against delusive attacks. Thus, the test accuracy decreased by delusive attacks can be largely recovered by adversarial training. To further understand the internal mechanism of the defense, we disclose that adversarial training can resist the delusive perturbations by preventing the learner from overly relying on non-robust features in a natural setting. Finally, we complement our theoretical findings with a set of experiments on popular benchmark datasets, which show that the defense withstands six different practical attacks. Both theoretical and empirical results vote for adversarial training when confronted with delusive adversaries.

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arXiv 2022年

作者： Ma, Zhongchen Chen, Songcan The School of Computer Science & communications Engineering Jiangsu University The College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Zhenjiang212013 China The College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics The MIIT Key Laboratory of Pattern Analysis and Machine Intelligence Nanjing211106 China

—Similarity-based method gives rise to a new class of methods for multi-label learning and also achieves promising performance. In this paper, we generalize this method, resulting in a new framework for classification task. Specifically, we unite similarity-based learning and generalized linear models to achieve the best of both worlds. This allows us to capture interdependencies between classes and prevent from impairing performance of noisy classes. Each learned parameter of the model can reveal the contribution of one class to another, providing interpretability to some extent. Experiment results show the effectiveness of the proposed approach on multi-class and multi-label datasets. Copyright © 2022, The Authors. All rights reserved.

关键词： machine learning

Centralized Feature Pyramid for Object Detection

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arXiv 2022年

作者： Quan, Yu Zhang, Dong Zhang, Liyan Tang, Jinhui The School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China The College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics MIIT Key Laboratory of Pattern Analysis and Machine Intelligence Collaborative Innovation Center of Novel Software Technology and Industrialization Nanjing211106 China

Visual feature pyramid has shown its superiority in both effectiveness and efficiency in a wide range of applications. However, the existing methods exorbitantly concentrate on the inter-layer feature interactions but ignore the intra-layer feature regulations, which are empirically proved beneficial. Although some methods try to learn a compact intra-layer feature representation with the help of the attention mechanism or the vision transformer, they ignore the neglected corner regions that are important for dense prediction tasks. To address this problem, in this paper, we propose a Centralized Feature Pyramid (CFP) for object detection, which is based on a globally explicit centralized feature regulation. Specifically, we first propose a spatial explicit visual center scheme, where a lightweight MLP is used to capture the globally long-range dependencies and a parallel learnable visual center mechanism is used to capture the local corner regions of the input images. Based on this, we then propose a globally centralized regulation for the commonly-used feature pyramid in a top-down fashion, where the explicit visual center information obtained from the deepest intra-layer feature is used to regulate frontal shallow features. Compared to the existing feature pyramids, CFP not only has the ability to capture the global long-range dependencies, but also efficiently obtain an all-round yet discriminative feature representation. Experimental results on the challenging MS-COCO validate that our proposed CFP can achieve the consistent performance gains on the state-of-the-art YOLOv5 and YOLOX object detection baselines. The code has been released at: CFPNet. Copyright © 2022, The Authors. All rights reserved.

关键词： Object detection

Coarse-to-fine Foreground Segmentation based on Co-occurrence Pixel-Block and Spatio-Temporal Attention Model

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Coarse-to-fine Foreground Segmentation based on Co-occurrenc...

International Conference on pattern Recognition

作者： Dong Liang Xinyu Liu College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics MIIT Key Laboratory of Pattern Analysis and Machine Intelligence Collaborative Innovation Center of Novel Software Technology and Industrialization Nanjing China

ISBN: (纸本)9781728188089;9781728188096

Foreground segmentation in dynamic scene is an important task in video surveillance. The unsupervised background subtraction method based on background statistics modeling has difficulties in updating. On the other hand, the supervised foreground segmentation method based on deep learning relies on the large-scale of accurately annotated training data, which limits its cross-scene performance. In this paper, we propose a foreground segmentation method from coarse to fine. First, a across-scenes trained Spatio-Temporal Attention Model (STAM) is used to achieve coarse segmentation, which does not require training on specific scene. Then the coarse segmentation is used as a reference to help Co-occurrence Pixel-Block Model (CPB) complete the fine segmentation, and at the same time help CPB to update its background model. This method is more flexible than those deep-learning-based methods which depends on the specific-scene training, and realizes the accurate online dynamic update of the background model. Experimental results on WallFlower and LIMU validate our method outperforms STAM, CPB and other methods of participating in comparison.

关键词： Training Deep learning Training data Video surveillance pattern recognition Task analysis

ACRM: Attention Cascade R-CNN with Mix-NMS for Metallic Surface Defect Detection

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ACRM: Attention Cascade R-CNN with Mix-NMS for Metallic Surf...

International Conference on pattern Recognition

作者： Junting Fang Xiaoyang Tan Yuhui Wang College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics MIIT Key Laboratory of Pattern Analysis and Machine Intelligence Collaborative Innovation Center of Novel Software Technology and Industrialization Nanjing China

Metallic surface defect detection is of great significance in quality control for production. However, this task is very challenging due to the noise disturbance, large appearance variation, and the ambiguous definition of the defect individual. Traditional image processing methods are unable to detect the damaged region effectively and efficiently. In this paper, we propose a new defect detection method, Attention Cascade R-CNN with Mix-NMS (ACRM), to classify and locate defects robustly. Three submodules are developed to achieve this goal: 1) a lightweight attention block is introduced, which can improve the ability in capture global and local feature both in the spatial and channel dimension; 2) we firstly apply the cascade R-CNN to our task, which exploits multiple detectors to sequentially refine the detection result robustly; 3) we introduce a new method named Mix Non-Maximum Suppression (Mix-NMS), which can significantly improve its ability in filtering the redundant detection result in our task. Extensive experiments on a real industrial dataset show that ACRM achieves state-of-the-art results compared to the existing methods, demonstrating the effectiveness and robustness of our detection method.

关键词： Filtering Quality control Production Detectors Feature extraction Robustness pattern recognition

Improving model robustness by adaptively correcting perturbation levels with active queries

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arXiv 2021年

作者： Ning, Kun-Peng Tao, Lue Chen, Songcan Huang, Sheng-Jun College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics MIIT Key Laboratory of Pattern Analysis and Machine Intelligence Collaborative Innovation Center of Novel Software Technology and Industrialization Nanjing211106 China

In addition to high accuracy, robustness is becoming increasingly important for machine learning models in various applications. Recently, much research has been devoted to improving the model robustness by training with noise perturbations. Most existing studies assume a fixed perturbation level for all training examples, which however hardly holds in real tasks. In fact, excessive perturbations may destroy the discriminative content of an example, while deficient perturbations may fail to provide helpful information for improving the robustness. Motivated by this observation, we propose to adaptively adjust the perturbation levels for each example in the training process. Specifically, a novel active learning framework is proposed to allow the model to interactively query the correct perturbation level from human experts. By designing a cost-effective sampling strategy along with a new query type, the robustness can be significantly improved with a few queries. Both theoretical analysis and experimental studies validate the effectiveness of the proposed approach. © 2021, CC BY.

关键词： Cost effectiveness

Deep Learning in Palmprint Recognition-A Comprehensive Survey

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arXiv 2025年

作者： Gao, Chengrui Yang, Ziyuan Jia, Wei Leng, Lu Zhang, Bob Teoh, Andrew Beng Jin College of Computer Science Sichuan University Chengdu610065 China Singapore School of Computer and Information Hefei University of Technology Hefei China Jiangxi Provincial Key Laboratory of Image Processing and Pattern Recognition Nanchang Hangkong University Nanchang China Pattern Analysis and Machine Intelligence Group Department of Computer and Information Science University of Macau Taipa China School of Electrical and Electronic Engineering College of Engineering Yonsei University Seoul Korea Republic of

Palmprint recognition has emerged as a prominent biometric technology, widely applied in diverse scenarios. Traditional handcrafted methods for palmprint recognition often fall short in representation capability, as they heavily depend on researchers' prior knowledge. Deep learning (DL) has been introduced to address this limitation, leveraging its remarkable successes across various domains. While existing surveys focus narrowly on specific tasks within palmprint recognition-often grounded in traditional methodologies-there remains a significant gap in comprehensive research exploring DL-based approaches across all facets of palmprint recognition. This paper bridges that gap by thoroughly reviewing recent advancements in DL-powered palmprint recognition. The paper systematically examines progress across key tasks, including region-of-interest segmentation, feature extraction, and security/privacy-oriented challenges. Beyond highlighting these advancements, the paper identifies current challenges and uncovers promising opportunities for future research. By consolidating state-of-the-art progress, this review serves as a valuable resource for researchers, enabling them to stay abreast of cutting-edge technologies and drive innovation in palmprint recognition. Copyright © 2025, The Authors. All rights reserved.

关键词： Biometrics

Open-set label noise can improve robustness against inherent label noise

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arXiv 2021年

作者： Wei, Hongxin Tao, Lue Xie, Renchunzi An, Bo School of Computer Science and Engineering Nanyang Technological University Singapore College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics China MIIT key Laboratory of Pattern Analysis and Machine Intelligence China

Learning with noisy labels is a practically challenging problem in weakly supervised learning. In the existing literature, open-set noises are always considered to be poisonous for generalization, similar to closed-set noises. In this paper, we empirically show that open-set noisy labels can be non-toxic and even benefit the robustness against inherent noisy labels. Inspired by the observations, we propose a simple yet effective regularization by introducing Open-set samples with Dynamic Noisy Labels (ODNL) into training. With ODNL, the extra capacity of the neural network can be largely consumed in a way that does not interfere with learning patterns from clean data. Through the lens of SGD noise, we show that the noises induced by our method are random-direction, conflict-free and biased, which may help the model converge to a flat minimum with superior stability and enforce the model to produce conservative predictions on Out-of-Distribution instances. Extensive experimental results on benchmark datasets with various types of noisy labels demonstrate that the proposed method not only enhances the performance of many existing robust algorithms but also achieves significant improvement on Out-of-Distribution detection tasks even in the label noise setting. Copyright © 2021, The Authors. All rights reserved.

关键词： Benchmarking

Open-set label noise can improve robustness against inherent label noise 21

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Open-set label noise can improve robustness against inherent...

Proceedings of the 35th International Conference on Neural Information Processing Systems

作者： Hongxin Wei Lue Tao Renchunzi Xie Bo An School of Computer Science and Engineering Nanyang Technological University Singapore College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics China and MIIT key Laboratory of Pattern Analysis and Machine Intelligence China

ISBN: (纸本)9781713845393

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