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Unsupervised Outlier Detection via Transformation Invariant autoencoder

IEEE ACCESS 2021年 9卷 43991-44002页

作者： Cheng, Zhen Zhu, En Wang, Siqi Zhang, Pei Li, Wang Natl Univ Def Technol Sch Comp Changsha 410073 Peoples R China

autoencoder based methods are the majority of deep unsupervised outlier detection methods. However, these methods perform not well on complex image datasets and suffer from the noise introduced by outliers, especially when the outlier ratio is high. In this paper, we propose a framework named Transformation Invariant autoencoder (TIAE), which can achieve stable and high performance on unsupervised outlier detection. First, instead of using a conventional autoencoder, we propose a transformation invariant autoencoder to do better representation learning for complex image datasets. Next, to mitigate the negative effect of noise introduced by outliers and stabilize the network training, we select the most confident inliers likely examples in each epoch as the training set by incorporating adaptive self-paced learning in our TIAE framework. Extensive evaluations show that TIAE significantly advances unsupervised outlier detection performance by up to 10% AUROC against other autoencoder based methods on five image datasets.

关键词： Anomaly detection Training Image reconstruction Image restoration Deep learning Data models Task analysis Deep Learning unsupervised outlier detection autoencoder transformation invariant autoencoder

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ASSAF: Advanced and Slim StegAnalysis Detection Framework for JPEG images based on deep convolutional denoising autoencoder and Siamese networks

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NEURAL NETWORKS 2020年 131卷 64-77页

作者： Cohen, Assaf Cohen, Aviad Nissim, Nir Ben Gurion Univ Negev Cyber Secur Res Ctr Malware Lab Beer Sheva Israel Ben Gurion Univ Negev Dept Software & Informat Syst Engn Beer Sheva Israel Ben Gurion Univ Negev Dept Ind Engn & Management Beer Sheva Israel

Steganography is the art of embedding a confidential message within a host message. Modern steganography is focused on widely used multimedia file formats, such as images, video files, and Internet protocols. Recently, cyber attackers have begun to include steganography (for communication purposes) in their arsenal of tools for evading detection. Steganalysis is the counter-steganography domain which aims at detecting the existence of steganography within a host file. The presence of steganography in files raises suspicion regarding the file itself, as well as its origin and receiver, and might be an indication of a sophisticated attack. The JPEG file format is one of the most popular image file formats and thus is an attractive and commonly used carrier for steganography embedding. State-of-the-art JPEG steganalysis methods, which are mainly based on neural networks, are limited in their ability to detect sophisticated steganography use cases. In this paper, we propose ASSAF, a novel deep neural network architecture composed of a convolutional denoising autoencoder and a Siamese neural network, specially designed to detect steganography in JPEG images. We focus on detecting the J-UNIWARD method, which is one of the most sophisticated adaptive steganography methods used today. We evaluated our novel architecture using the BOSSBase dataset, which contains 10,000 JPEG images, in eight different use cases which combine different JPEG's quality factors and embedding rates (bpnzAC). Our results show that ASSAF can detect stenography with high accuracy rates, outperforming, in all eight use cases, the state-of-the-art steganalysis methods by 6% to 40%. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： Steganography Steganalysis Deep learning autoencoder Siamese neural network Convolution neural network

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Multitask autoencoder Model for Recovering Human Poses

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IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 2018年第6期65卷 5060-5068页

作者： Yu, Jun Hong, Chaoqun Rui, Yong Tao, Dacheng Hangzhou Dianzi Univ Dept Comp Sci Hangzhou 310018 Zhejiang Peoples R China Xiamen Univ Technol Xiamen 361005 Peoples R China Lenovo Grp Ltd Beijing 100085 Peoples R China Univ Sydney Fac Engn & Informat Technol UBTECH Sydney Artificial Intelligence Ctr Darlington NSW 2008 Australia Univ Sydney Fac Engn & Informat Technol Sch Informat Technol Darlington NSW 2008 Australia

Human pose recovery in videos is usually conducted by matching 2-D image features and retrieving relevant 3-D human poses. In the retrieving process, the mapping between images and poses is critical. Traditional methods assume this mapping relationship as local joint detection or global joint localization, which limits recovery performance of these methods since this two tasks are actually unified. In this paper, we propose a novel pose recovery framework by simultaneously learning the tasks of joint localization and joint detection. To obtain this framework, multiple manifold learning is used and the shared parameter is calculated. With them, multiple manifold regularizers are integrated and generalized eigendecomposition is utilized to achieve parameter optimization. In this way, pose recovery is boosted by both global mapping and local refinement. Experimental results on two popular datasets demonstrates that the recovery error has been reduced by 10%-20%, which proves the performance improvement of the proposed method.

关键词： autoencoder deep learning human pose recovery (HPR) manifold learning multitask learning (MTL)

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SILP-autoencoder for face de-occlusion

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NEUROCOMPUTING 2022年第0期485卷 47-56页

作者： Sun, Dengdi Xie, Wandong Ding, Zhuanlian Tang, Jin Anhui Univ Sch Artificial Intelligence Key Lab Intelligent Comp & Signal Proc ICSP Minist Educ Hefei 230601 Peoples R China Anhui Univ Sch Comp Sci & Technol Anhui Prov Key Lab Multimodal Cognit Comp Hefei 230601 Peoples R China Anhui Univ Sch Internet Hefei 230039 Peoples R China

Recognizing faces with partial occlusion is a challenging problem in many real-world applications. Although various methods have been proposed to deal with the facial image de-occlusion tasks, most of them only concern the local features of occluded images, obviously ignoring the global facial expressions and structural prior information. In this paper, we propose a novel end-to-end SILP-autoencoder to effectively restore partial occluded faces. To improve the recovery quality and occlusion removal robustness, our framework mainly consists of two components, Laplacian prior subnetwork, and left-and-right symmetric match module (LR-match module), which preserve the global facial expression features and fully make use of the symmetrical characteristics of facial regions and structures respectively. Based on the above characteristics, a composite loss function is designed to achieve end-to-end training of the entire network. Extensive experiments on the face expression datasets with various shaded areas suggest that our approach achieves superior performance against the state-of-the-art methods. In particular, our method is more useful for facial detail recovery and distortion expression suppression. (c) 2022 Elsevier B.V. All rights reserved.

关键词： Face de-occlusion autoencoder Facial expression

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Human Action Recognition Based on a Spatio-Temporal Video autoencoder

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INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE 2020年第11期34卷

作者： Sousa e Santos, Anderson Carlos Pedrini, Helio Univ Estadual Campinas Inst Comp BR-13083852 Campinas SP Brazil

Due to rapid advances in the development of surveillance cameras with high sampling rates, low cost, small size and high resolution, video-based action recognition systems have become more commonly used in various computer vision applications. Human operators can be supported with the aid of such systems to detect events of interest in video sequences, improving recognition results and reducing failure cases. In this work, we propose and evaluate a method to learn two-dimensional (2D) representations from video sequences based on an autoencoder framework. Spatial and temporal information is explored through a multi-stream convolutional neural network in the context of human action recognition. Experimental results on the challenging UCF101 and HMDB51 datasets demonstrate that our representation is capable of achieving competitive accuracy rates when compared to other approaches available in the literature.

关键词： Human action recognition autoencoder convolutional neural networks spatio-temporal information multi-stream networks

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Dual autoencoder Network for Retinex-Based Low-Light Image Enhancement

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IEEE ACCESS 2018年 6卷 22084-22093页

作者： Park, Seonhee Yu, Soohwan Kim, Minseo Park, Kwanwoo Paik, Joonki Chung Ang Univ Grad Sch Adv Imaging Sci Multimedia & Film Dept Image Seoul 06974 South Korea

This paper presents a dual autoencoder network model based on the retinex theory to perform the low-light enhancement and noise reduction by combining the stacked and convolutional autoencoders. The proposed method first estimates the spatially smooth illumination component which is brighter than an input low-light image using a stacked autoencoder with a small number of hidden units. Next, we use a convolutional autoencoder which deals with 2-D image information to reduce the amplified noise in the brightness enhancement process. We analyzed and compared roles of the stacked and convolutional autoencoders with the constraint terms of the variational retinex model. In the experiments, we demonstrate the performance of the proposed algorithm by comparing with the state-of-the-art existing low-light and contrast enhancement methods.

关键词： autoencoder image processing image enhancement neural networks variational retinex model unsupervised learning

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Sparse autoencoder-based feature extraction from TOF-SIMS image data of human skin structures

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ANALYTICAL AND BIOANALYTICAL CHEMISTRY 2022年第2期414卷 1177-1186页

作者： Matsuda, Kazuhiro Aoyagi, Satoka Toray Res Ctr Ltd Surface Sci Labs 3-3-7 Sonoyama Otsu Shiga 5208567 Japan Seikei Univ Fac Sci & Technol 3-3-1 Kichijoji Kitamachi Musashino Tokyo 1808633 Japan

Time-of-flight secondary ion mass spectrometry (TOF-SIMS) is a useful and versatile tool for surface analysis, enabling detailed compositional information to be obtained for the surfaces of diverse samples. Furthermore, in the case of two- or three-dimensional imaging, the measurement sensitivity in the higher molecular weight range can be improved by using a cluster ion source, thus further enriching the TOF-SIMS information. Therefore, appropriate analytical methods are required to interpret this TOF-SIMS data. This study explored the capabilities of a sparse autoencoder, a feature extraction method based on artificial neural networks, to process TOF-SIMS image data. The sparse autoencoder was applied to TOF-SIMS images of human skin keratinocytes to extract the distribution of endogenous intercellular lipids and externally penetrated drugs. The results were compared with those obtained using principal component analysis (PCA) and multivariate curve resolution (MCR), which are conventionally used for extracting features from TOF-SIMS data. This confirmed that the sparse autoencoder matches, and often betters, the feature extraction performance of conventional methods, while also offering greater flexibility.

关键词： TOF-SIMS autoencoder Feature extraction Artificial neural network Bioimaging

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Residual Dense autoencoder Network for Nonlinear Hyperspectral Unmixing

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2022年 15卷 5580-5595页

作者： Yang, Xu Chen, Jianguo Wang, Chengbin Chen, Zihao China Univ Geosci State Key Lab Geol Proc & Mineral Resources Wuhan 430074 Peoples R China China Univ Geosci Sch Earth Resources Wuhan 430074 Peoples R China Wuhan Univ Sch Remote Sensing & Informat Engn Wuhan 430074 Peoples R China

Hyperspectral unmixing is a popular research topic in hyperspectral processing, aiming at obtaining the ground features contained in the mixed pixels and their proportion. Recently, nonlinear mixing models have received particular attention in hyperspectral decomposition since the linear mixing model cannot suitably apply in the situation that exists in multiple scattering. In this article, we constructed a residual dense autoencoder network (RDAE) for nonlinear hyperspectral unmixing in multiple scattering scenarios. First, an encoder was built based on the residual dense network (RDN) and attention layer. The RDN is employed to characterize multiscale representations, which are further transformed with the attention layer to estimate the abundance maps. Second, we designed a decoder based on the unfolding of a generalized bilinear model to extract endmembers and estimate their second-order scattering interactions. Comparative experiments between the RDAE and six other state-of-the-art methods under synthetic and real hyperspectral datasets demonstrate that the proposed method achieved a better performance in terms of endmember extraction and abundance estimation.

关键词： Hyperspectral imaging Feature extraction Decoding Spatial resolution Scattering Licenses Kernel autoencoder generalized bilinear model (GBM) nonlinear hyperspectral unmixing residual dense network (RDN)

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Hyperspectral anomaly detection based on autoencoder and spatial morphology extraction

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JOURNAL OF APPLIED REMOTE SENSING 2021年第3期15卷

作者： Feng, Jing Zhang, Liyan Capital Normal Univ Coll Resource Environm & Tourism Key Lab 3D Informat Acquisit & Applicat Minist Beijing Peoples R China

Anomaly detection (AD) is a crucial task for detecting salient objects in cluttered backgrounds. Classical AD algorithms based on statistical models or geometric models have achieved acceptable detection results. However, most of them do not hierarchically extract deep features or consider the spatial structure of the images. We propose a method that combines the reconstruction error of autoencoder (AE) and spatial morphological characteristics to estimate anomalousness. The reconstruction errors and the spatially dominant information are comprehensively considered. Specifically, given the compression capability of the AE, we use the dimensionality reduction results obtained by encoding for analyzing local pixel difference;an adaptive dual window is employed in this process. The morphological transformation commonly used in edge detection is utilized to refine the small space anomalies. Experimental results on different hyperspectral images show that the proposed AE and spatial morphology extraction-based approach significantly surpasses several traditional alternatives. (C) 2021 Society of Photo-Optical Instrumentation Engineers (SPIE)

关键词： hyperspectral anomaly detection autoencoder deep learning feature extraction morphological transformation

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A Trust Evaluation Mechanism Based on autoencoder Clustering Algorithm for Edge Device Access of IoT

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Computers, Materials & Continua 2024年第2期78卷 1881-1895页

作者： Xiao Feng Zheng Yuan Cyber Security Academy Beijing University of Posts and TelecommunicationsBeijing100876China Department of Cyberspace Security Beijing Electronic Science and Technology InstituteBeijing100070China State Grid Info-Telecom Great Power Science and Technology Co. Ltd.Beijing102211China

First,we propose a cross-domain authentication architecture based on trust evaluation mechanism,including registration,certificate issuance,and cross-domain authentication processes.A direct trust evaluation mechanism based on the time decay factor is proposed,taking into account the influence of historical interaction *** weight the time attenuation factor to each historical interaction record for updating and got the new historical record *** refer to the beta distribution to enhance the flexibility and adaptability of the direct trust assessment model to better capture time trends in the historical *** we propose an autoencoder-based trust clustering *** perform feature extraction based on *** leibler(KL)divergence is used to calculate the reconstruction *** constructing a convolutional autoencoder,we introduce convolutional neural networks to improve training efficiency and introduce sparse constraints into the hidden layer of the *** sparse penalty term in the loss function measures the difference through the KL *** clustering is performed based on the density based spatial clustering of applications with noise(DBSCAN)clustering *** the clustering process,edge nodes have a variety of trustworthy attribute *** assign different attribute weights according to the relative importance of each attribute in the clustering process,and a larger weight means that the attribute occupies a greater weight in the calculation of ***,we introduced adaptive weights to calculate comprehensive trust *** experiments prove that our trust evaluation mechanism has excellent reliability and accuracy.

关键词： Cross-domain authentication trust evaluation autoencoder

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