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Adversarial Defense Based on Denoising convolutional autoencoder in EEG-Based Brain-Computer Interfaces

IEEE ACCESS 2024年 12卷 146441-146452页

作者： Ding, Yongting Li, Lin Li, Qingyan Jimei Univ Sch Sci Xiamen 361021 Peoples R China Jimei Univ Comp Engn Coll Xiamen 361021 Peoples R China Jimei Univ Digital Fujian Big Data Modeling & Intelligent Com Xiamen 361021 Peoples R China

The exploration and implementation of brain-computer interfaces (BCIs) utilizing electro-encephalography (EEG) are becoming increasingly widespread. However, their safety considerations have received scant attention. Recent studies have shown that EEG-based BCIs are vulnerable to adversarial attacks. Remarkably, only a limited amount of literature has addressed adversarial defense strategies against EEG-based BCIs. This study introduces a defense approach based on autoencoders, termed the Denoising convolutional autoencoder (DCAE), which serves as a preprocessing unit preceding the classification model. The DCAE aims to mitigate adversarial disturbances prior to inputting samples into the classifier, thereby preserving the classifier's original structure. Experiments were conducted using two different EEG datasets and three convolutional neural network (CNN) models to evaluate the effectiveness of DCAE. The experimental results show that the proposed method can achieve better defense effect in most cases against various adversarial attack methods. Additionally, the sensitivity of the DCAE to different magnitudes of perturbation was evaluated. The findings indicate that the robustness of DCAE is not affected by the variation of attack intensity, a characteristic not observed in existing defense strategies for EEG-based BCIs. It is our aspiration that these results will advance the frontier of research on defending EEG-based BCIs against adversarial threats.

关键词： Brain-computer interface EEG adversarial defense denoising convolutional autoencoder

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A convolutional autoencoder Based Fault Detection Method for Metro Railway Turnout

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Computer Modeling in Engineering & Sciences 2023年第7期136卷 471-485页

作者： Chen Chen Xingqiu Li Kai Huang Zhongwei Xu Meng Mei School of Electronic and Information Engineering Tongji UniversityShanghai201804China School of Engineering City University of Hong KongHong Kong200433China School of Civil Aviation Northwestern Polytechnical UniversityXi’an710072China School of Computer Engineering Jimei UniversityXiamen361021China

Railway turnout is one of the critical equipment of Switch&Crossing(S&C)Systems in railway,related to the train’s safety and operation *** the advancement of intelligent sensors,data-driven fault detection technology for railway turnout has become an important research ***,little research in the literature has investigated the capability of data-driven fault detection technology for metro railway *** paper presents a convolutional autoencoder-based fault detection method for the metro railway turnout considering human field inspection ***,the one-dimensional original time-series signal is converted into a twodimensional image by data pre-processing and 2D ***,a binary classification model based on the convolutional autoencoder is developed to implement fault *** profile and structure information can be captured by processing data as *** performance of our method is evaluated and tested on real-world operational current data in themetro *** results show that the proposedmethod achieves better performance,especially in terms of error rate and specificity,and is robust in practical engineering applications.

关键词： convolutional autoencoder fault detection metro railway turnout

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One-Class Classification in Images and Videos Using a convolutional autoencoder With Compact Embedding

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IEEE ACCESS 2020年 8卷 86520-86535页

作者： Ribeiro, Manasses Gutoski, Matheus Lazzaretti, Andre E. Lopes, Heitor S. Catarinense Fed Inst Educ Sci & Technol IFC BR-89560000 Videira Brazil Fed Univ Technol Parana UTFPR BR-3165 Curitiba Parana Brazil

In One-Class Classification (OCC) problems, the classifier is trained with samples of a class considered normal, such that exceptional patterns can be identified as anomalies. Indeed, for real-world problems, the representation of the normal class in the feature space is an important issue, considering that one or more clusters can describe different aspects of the normality. For classification purposes, it is important that these clusters be as compact (dense) as possible, for better discriminating anomalous patterns, which is a recurrent problem in OCC tasks. This work introduces a hybrid approach using deep learning and One-Class Support Vector Machine (OC-SVM) methods, named convolutional autoencoder with Compact Embedding (CAE-CE), for enhancing the compactness of clusters in the feature space. Such an approach is still underexplored in the literature, being restricted to models within the context of metric learning. Additionally, the absence of anomalous samples during training makes it difficult to determine when to interrupt the learning process, so as to avoid over-compression of the normal examples, thus resulting in overfitting of the model. In this work, we propose a novel sensitivity-based stop criterion, and its suitability for OCC problems was assessed. Using an OC-SVM for the classification task, several experiments were done using publicly available image and video datasets. We also introduce other two new benchmarks, specifically designed for video anomaly detection in highways. The final performance of the proposed method was compared with a baseline convolutional autoencoder (CAE). Overall results suggest that the enhanced compactness introduced by the CAE-CE improved the classification performance for most datasets. Also, the qualitative analysis of frames at the visual level indicated that features learned by CAE-CE are closely correlated to the anomalous events.

关键词： Anomaly detection compact embedding convolutional autoencoder deep learning feature extraction one-class classification

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Optical Fiber Fault Detection and Localization in a Noisy OTDR Trace Based on Denoising convolutional autoencoder and Bidirectional Long Short-Term Memory

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JOURNAL OF LIGHTWAVE TECHNOLOGY 2022年第8期40卷 2254-2264页

作者： Abdelli, Khouloud Griesser, Helmut Tropschug, Carsten Pachnicke, Stephan ADVA Opt Networking SE D-82152 Munich Germany Kiel Univ CAU Chair Commun D-24143 Kiel Germany

Optical time-domain reflectometry (OTDR) has been widely used for characterizing fiber optical links and for detecting and locating fiber faults. OTDR traces are prone to be distorted by different kinds of noise, causing blurring of the backscattered signals, and thereby leading to a misleading interpretation and a more cumbersome event detection task. To address this problem, a novel method combining a denoising convolutional autoencoder (DCAE) and a bidirectional long short-term memory (BiLSTM) is proposed, whereby the former is used for noise removal of OTDR signals and the latter for fault detection, localization, and diagnosis with the denoised signal as input. The proposed approach is applied to noisy OTDR signals of different levels of input SNR ranging from -5 dB to 15 dB. The experimental results demonstrate that: (i) the DCAE is efficient in denoising the OTDR traces and it outperforms other deep learning techniques and the conventional denoising methods;and (ii) the BiLSTM achieves a high detection and diagnostic accuracy of 96.7% with an improvement of 13.74% compared to the performance of the same model trained with noisy OTDR signals.

关键词： Computer architecture Noise reduction Optical fibers Microprocessors Decoding Logic gates Convolution Bidirectional long short-term memory convolutional autoencoder denoising fiber optics intelligent fault detection and diagnosis OTDR (optical time domain reflectometry)

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DSCAE: a denoising sparse convolutional autoencoder defense against adversarial examples

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JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING 2022年第3期13卷 1419-1429页

作者： Ye, Hongwei Liu, Xiaozhang Li, Chunlai Hainan Univ Sch Comp Sci & Cyberspace Secur Haikou Hainan Peoples R China Heyuan Polytech Sch Elect & Informat Engn Heyuan Peoples R China

Deep neural networks are a state-of-the-art method used to computer vision. Imperceptible perturbations added to benign images can induce the deep learning network to make incorrect predictions, though the perturbation is imperceptible to human eyes. Those adversarial examples threaten the safety of deep learning model in many real-world applications. In this work, we proposed a method called denoising sparse convolutional autoencoder (DSCAE) to defense against the adversarial perturbations. This is a preprocessing module works before the classification model, which can remove substantial amounts of the adversarial noise. The DSCAE defense has been evaluated against FGSM, DeepFool, C&W, JSMA attacks on the MNIST and CIFAR-10 datasets. The experimental results show that DSCAE defends against state-of-art attacks effectively.

关键词： Adversarial example Adversarial attack convolutional autoencoder Classification

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A hybrid method for day-ahead photovoltaic power forecasting based on generative adversarial network combined with convolutional autoencoder

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IET RENEWABLE POWER GENERATION 2023年第3期17卷 644-658页

作者： Pan, Xueping Zhou, Jieyang Sun, Xiaorong Cao, Yang Cheng, Xiaomei Farahmand, Hossein HoHai Univ Coll Energy & Elect Engn Nanjing 210098 Peoples R China Yara Int Dept Res & Dev Oslo Norway Norwegian Univ Sci & Technol Dept Elect Power Engn Trondheim Norway

Photovoltaic (PV) generation has high impact on the decarbonization pathways of power systems. Accuracy of day-ahead PV power forecasting has become crucial in the operation and control of power system with high PV penetration. This paper develops a hybrid approach based on generative adversarial network (GAN) combined with convolutional autoencoder (CAE) to improve PV power forecasting accuracy. Self-organizing map method is first utilized as data pre-processing to classify target days into different weather types based on solar irradiance. With the ability of GAN to reduce the burden of loss and the advantages of CAE to extract multi-scale effective features from the weather and PV power, PV power forecasting model consisting of GAN and CAE is proposed. The developed method has been tested on a real dataset in a Chinese PV station and compared with base reference PV forecasting methods. Numerical testing results demonstrate the effectiveness of our method with high accuracy.

关键词： feature extraction power system day-ahead photovoltaic power forecasting high PV penetration Power system management, operation and economics Solar power stations and photovoltaic power systems PV power forecasting model convolutional autoencoder Chinese PV station CAE photovoltaic generation decarbonization pathways different weather types hybrid method Neural nets load forecasting generative adversarial network data pre-processing Power system planning and layout self-organizing map method GAN PV power forecasting accuracy Power engineering computing power engineering computing photovoltaic power systems target days base reference PV forecasting methods hybrid approach

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CAERS-CF: enhancing convolutional autoencoder recommendations through collaborative filtering

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KNOWLEDGE AND INFORMATION SYSTEMS 2024年第11期66卷 6717-6738页

作者： Ghadami, Amirhossein Tran, Thomas Univ Ottawa Sch Elect Engn & Comp Sci EECS 800 King Edward Ave Ottawa ON K1N 6N5 Canada

Recommendation systems are crucial in boosting companies' revenues by implementing various strategies to engage customers and encourage them to invest in products or services. Businesses constantly desire to enhance these systems through different approaches. One effective method involves using hybrid recommendation systems, known for their ability to create high-performance models. We introduce a hybrid recommendation system that leverages two types of recommendation systems: first, a novel deep learning-based recommendation system that utilizes users' and items' content data, and second, a traditional recommendation system that employs users' past behaviour data. We introduce a novel deep learning-based recommendation system called convolutional autoencoder recommendation system (CAERS). It uses a convolutional autoencoder (CAE) to capture high-order meaningful relationships between users' and items' content information and decode them to predict ratings. Subsequently, we design a traditional model-based collaborative filtering recommendation system (CF) that leverages users' past behaviour data, utilizing singular value decomposition (SVD). Finally, in the last step, we combine the two method's predictions with linear regression. We determine the optimal weight for each prediction generated by the collaborative filtering and the deep learning-based recommendation system. Our main objective is to introduce a hybrid model called CAERS-CF that leverages the strengths of the two mentioned approaches. For experimental purposes, we utilize two movie datasets to showcase the performance of CAERS-CF. Our model outperforms each constituent model individually and other state-of-the-art deep learning or hybrid models. Across both datasets, the hybrid CAERS-CF model demonstrates an average RMSE improvement of approximately 3.70% and an average MAE improvement of approximately 5.96% compared to the next best models.

关键词： Recommendation system Deep learning convolutional autoencoder Hybrid recommendation system Collaborative filtering

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Energy Compaction-Based Image Compression Using convolutional autoencoder

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IEEE TRANSACTIONS ON MULTIMEDIA 2020年第4期22卷 860-873页

作者： Cheng, Zhengxue Sun, Heming Takeuchi, Masaru Katto, Jiro Waseda Univ Grad Sch Fundamental Sci & Engn Dept Comp Sci & Commun Engn Tokyo 1698555 Japan Waseda Res Inst Sci & Engn Tokyo 1698555 Japan JST PRESTO 4-1-8 Honcho Kawaguchi Saitama 3320012 Japan

Image compression has been an important research topic for many decades. Recently, deep learning has achieved great success in many computer vision tasks, and its use in image compression has gradually been increasing. In this paper, we present an energy compaction-based image compression architecture using a convolutional autoencoder (CAE) to achieve high coding efficiency. Our main contributions include three aspects: 1) we propose a CAE architecture for image compression by decomposing it into several down(up)sampling operations;2) for our CAE architecture, we offer a mathematical analysis on the energy compaction property and we are the first work to propose a normalized coding gain metric in neural networks, which can act as a measurement of compression capability;3) based on the coding gain metric, we propose an energy compaction-based bit allocation method, which adds a regularizer to the loss function during the training stage to help the CAE maximize the coding gain and achieve high compression efficiency. The experimental results demonstrate our proposed method outperforms BPG (HEVC-intra), in terms of the MS-SSIM quality metric. Additionally, we achieve better performance in comparison with existing bit allocation methods, and provide higher coding efficiency compared with state-of-the-art learning compression methods at high bit rates.

关键词： Image compression convolutional autoencoder optimum bit allocation energy compaction

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Image denoising using deep convolutional autoencoder with feature pyramids

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TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES 2020年第4期28卷 2096-2109页

作者： Cetinkaya, Ekrem Kirac, M. Furkan Ozyegin Univ Dept Engn Fac Comp Sci Istanbul Turkey Alpen Adria Univ Klagenfurt ITEC Inst Informat Technol Klagenfurt Austria

Image denoising is 1 of the fundamental problems in the image processing field since it is the preliminary step for many computer vision applications. Various approaches have been used for image denoising throughout the years from spatial filtering to model-based approaches. Having outperformed all traditional methods, neural-network-based discriminative methods have gained popularity in recent years. However, most of these methods still struggle to achieve flexibility against various noise levels and types. In this paper, a deep convolutional autoencoder combined with a variant of feature pyramid network is proposed for image denoising. Simulated data generated by Blender software along with corrupted natural images are used during training to improve robustness against various noise levels. Experimental results show that the proposed method can achieve competitive performance in blind Gaussian denoising with significantly less training time required compared to state of the art methods. Extensive experiments showed the proposed method gives promising performance in a wide range of noise levels with a single network.

关键词： Image denoising convolutional autoencoder feature pyramid image processing

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Interference Suppression for an FM-Radio-Based Passive Radar via Deep convolutional autoencoder

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IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS 2024年第1期60卷 106-118页

作者： Park, Do-Hyun Park, Geun-Ho Park, Ji-Hun Bang, Jong-Hyeon Kim, Doohwan Kim, Hyoung-Nam Pusan Natl Univ Busan 46241 South Korea LIG Nex1 Seongnam Si 13488 South Korea

Recently, the passive bistatic radar (PBR) that exploits frequency modulation (FM) radio transmitters as illuminators, has witnessed widespread usage owing to its various advantages. However, the characteristics of FM-radio-based PBR result in interference components in the range-Doppler (RD) map, which may increase false alarms. Therefore, this study proposes a method for suppressing interference components using a deep learning approach. The two main contributions of this study are as follows. First, a convolutional autoencoder model capable of effectively suppressing interference in the RD map of the PBR was proposed. Second, a synthetic RD map dataset generation method that can enable the autoencoder to operate robustly in PBR in a real environment was presented. Further, a performance comparison between the proposed method and existing methods using simulated data proved that the deep learning-based method exhibited superior target detection performance. Furthermore, using the data recorded by the PBR in a real environment, the proposed autoencoder model was shown to effectively suppress interference components in a real interference environment.

关键词： Clutter Surveillance Doppler effect Convolution Detectors Radar Passive radar convolutional autoencoder deep learning interference suppression passive bistatic radar (PBR) range-Doppler (RD) map

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