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Deep one-class probability learning for end-to-end image classification

NEURAL NETWORKS 2025年 185卷 107201页

作者： Liu, Jia Zhang, Wenhua Liu, Fang Yang, Jingxiang Xiao, Liang Nanjing Univ Sci & Technol Sch Comp Sci & Engn Nanjing 210094 Peoples R China

one-class learning has many application potentials in novelty, anomaly, and outlier detection systems. It aims to distinguish both positive and negative samples with a model trained via only positive samples or one-class annotated samples. With the difficulty in training an end-to-end classification network, existing methods usually make decisions indirectly. To fully exploit the learning capability of a deep network, in this paper, we propose to design a deep end-to-end binary image classifier based on convolutional neural network with input of image and output of classification result. Without negative training samples, we establish a probabilistic model driven by an energy to learn the distribution of positive samples. The energy is proposed based on the output of the network which subtly models the deep discriminations into statistics. During optimization, to overcome the difficulty of distribution estimation, we propose a novel particle swarm optimization algorithm based sampling method. Compared with existing methods, the proposed method is able to directly output classification results without additional thresholding or estimating operations. Moreover, the deep network is directly optimized via the probabilistic model which results in better adaptation of positive distribution and classification task. Experiments demonstrate the effectiveness and state-of-the-art performance of the proposed method.

关键词： one-class learning Image classification Probabilistic model Deep neural network

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one-class learning and concept summarization for data streams

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KNOWLEDGE AND INFORMATION SYSTEMS 2011年第3期28卷 523-553页

作者： Zhu, Xingquan Ding, Wei Yu, Philip S. Zhang, Chengqi Univ Technol Sydney Ctr Quantum Computat & Intelligent Syst Fac Eng & Informat Technol Sydney NSW 2007 Australia Florida Atlantic Univ Dept Comp Sci & Engn Boca Raton FL 33431 USA Univ Massachusetts Dept Comp Sci Boston MA 02125 USA Univ Illinois Dept Comp Sci Chicago IL 60680 USA

In this paper, we formulate a new research problem of concept learning and summarization for one-class data streams. The main objectives are to (1) allow users to label instance groups, instead of single instances, as positive samples for learning, and (2) summarize concepts labeled by users over the whole stream. The employment of the batch-labeling raises serious issues for stream-oriented concept learning and summarization, because a labeled instance group may contain non-positive samples and users may change their labeling interests at any time. As a result, so the positive samples labeled by users, over the whole stream, may be inconsistent and contain multiple concepts. To resolve these issues, we propose a one-class learning and summarization (OCLS) framework with two major components. In the first component, we propose a vague one-class learning (VOCL) module for concept learning from data streams using an ensemble of classifiers with instance level and classifier level weighting strategies. In the second component, we propose a one-class concept summarization (OCCS) module that uses clustering techniques and a Markov model to summarize concepts labeled by users, with only one scanning of the stream data. Experimental results on synthetic and real-world data streams demonstrate that the proposed VOCL module outperforms its peers for learning concepts from vaguely labeled stream data. The OCCS module is also able to rebuild a high-level summary for concepts marked by users over the stream.

关键词： Stream data mining one-class learning classification Clustering

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one-class learning Method Based on Live Correlation Loss for Face Anti-Spoofing

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IEEE ACCESS 2020年 8卷 201635-201648页

作者： Lim, Seokjae Gwak, Yongjae Kim, Wonjun Roh, Jong-Hyuk Cho, Sangrae Konkuk Univ Dept Elect & Elect Engn Seoul 05029 South Korea Elect & Telecommun Res Inst Daejeon 34129 South Korea

As biometric authentication systems are popularly used in various mobile devices, e.g., smart-phones and tablets, face anti-spoofing methods have been actively developed for the high-level security. However, most previous approaches still suffer from diverse types of spoofing attacks, which are hardly covered by the limited number of training datasets, and thus they often show the poor accuracy when unseen samples are given for the test. To address this problem, a novel method for face anti-spoofing is proposed based on one-class (i.e., live face only) learning with the live correlation loss. Specifically, encoder-decoder networks are firstly trained with only live faces to extract latent features, which have an ability to compactly represent various live facial properties in the embedding space and produce the spoofing cues, which are simply obtained by subtracting the original RGB image and the generated one. After that, such features are fed into the proposed feature correlation network (FCN) so that weights of FCN learn to compute "liveness" of given features under the guidance of the live correlation loss. It is noteworthy that the proposed method only requires live facial images for training the model, which are easier to obtain than fake ones, and thus the generality power for resolving the problem of face anti-spoofing can be expected to be improved. Experimental results on various benchmark datasets demonstrate the efficiency and robustness of the proposed method.

关键词： Biometric authentication systems face anti-spoofing one-class learning live correlation loss feature correlation network

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one-class learning with Adaptive Centroid Shift for Audio Deepfake Detection 25

One-Class Learning with Adaptive Centroid Shift for Audio De...

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25th Interspeech Conference

作者： Kim, Hyun Myung Fang, Kangwook Kim, Hoirin Korea Adv Inst Sci & Technol Sch Elect Engn Daejeon South Korea

As speech synthesis systems continue to make remarkable advances in recent years, the importance of robust deepfake detection systems that perform well in unseen systems has grown. In this paper, we propose a novel adaptive centroid shift (ACS) method that updates the centroid representation by continually shifting as the weighted average of bonafide representations. Our approach uses only bonafide samples to define their centroid, which can yield a specialized centroid for one-class learning. Integrating our ACS with one-class learning gathers bonafide representations into a single cluster, forming well-separated embeddings robust to unseen spoofing attacks. Our proposed method achieves an equal error rate (EER) of 2.19% on the ASVspoof 2021 deepfake dataset, outperforming all existing systems. Furthermore, the t-SNE visualization illustrates that our method effectively maps the bonafide embeddings into a single cluster and successfully disentangles the bonafide and spoof classes.

关键词： audio deepfake detection one-class learning ASVspoof challenge anti-spoofing

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Malicious Overtones: Hunting Data Theft in the Frequency Domain with one-class learning

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ACM TRANSACTIONS ON PRIVACY AND SECURITY 2019年第4期22卷 1–34页

作者： Powell, Brian A. Johns Hopkins Univ Appl Phys Lab 11100 Johns Hopkins Rd Laurel MD 20723 USA

A method for detecting electronic data theft from computer networks is described, capable of recognizing patterns of remote exfiltration occurring over days to weeks. Normal traffic flow data, in the form of a host's ingress and egress bytes over time, is used to train an ensemble of one-class learners. The detection ensemble is modular, with individual classifiers trained on different traffic features thought to characterize malicious data transfers. We select features that model the egress to ingress byte balance over time, periodicity, short timescale irregularity, and density of the traffic. The features are most efficiently modeled in the frequency domain, which has the added benefit that variable duration flows are transformed to a fixed-size feature vector, and by sampling the frequency space appropriately, long-duration flows can be tested. When trained on days or weeks worth of traffic from individual hosts, our ensemble achieves a low false-positive rate (<2%) on a range of different system types. Simulated exfiltration samples with a variety of different timing and data characteristics were generated and used to test ensemble performance on different kinds of systems: When trained on a client workstation's external traffic, the ensemble was generally successful at detecting exfiltration that is not simultaneously ingress-heavy, connection-sparse, and of short duration-a combination that is not optimal for attackers seeking to transfer large amounts of data. Remote exfiltration is more difficult to detect from egress-heavy systems, like web servers, with normal traffic exhibiting timing characteristics similar to a wide range of exfiltration types.

关键词： Data exfiltration one-class learning

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Incrementally Generative Adversarial Diagnostics Using Few-Shot Enabled one-class learning

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2024年第10期20卷 12189-12199页

作者： Pu, Ziqiang Yan, Lijuan Bai, Yun Cabrera, Diego Li, Chuan Chongqing Technol & Business Univ Sch Mech Engn Chongqing 400067 Peoples R China Chongqing Technol & Business Univ Res Ctr Syst Hlth Maintenance Chongqing 400067 Peoples R China Chongqing Technol & Business Univ Sch Management Sci & Engn Chongqing 400067 Peoples R China Univ Politecn Salesiana GIDTEC Grp Cuenca 010105 Ecuador

In real-world industrial scenarios, fault diagnosis often relies on a significant volume of normal data to detect faults with a few coming samples. The limited sample nature of newly introduced faults can pose challenges for deep learning-based fault diagnosis models. To address this issue, a novel framework named incrementally generative adversarial diagnostics was developed by using few-shot enabled one-class learning (FSEOCL) for effective anomaly detection and fault classification of the new-coming few samples. In the addressed method, a bi-directional generative adversarial network is first trained using only normal data to acquire an encoder for latent representation from time-series data. A few samples of each fault condition in latent space can be then identified and classified correctly through FSEOCL. The effectiveness of the proposed framework is demonstrated through fault diagnosis experiments conducted on both a benchmark bearing and an industrial robot. The results underscore the adaptability of this framework to industrial fault diagnosis scenarios, highlighting its ability to achieve accurate anomaly detection and fault classification compared to state-of-the-art peer methods.

关键词： Anomaly detection Generators Proposals Kernel Fault detection Bidirectional generative adversarial network fault diagnosis few-shot learning incremental learning one-class learning incremental learning one-class learning

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Import Vector Domain Description: A Kernel Logistic one-class learning Algorithm

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND learning SYSTEMS 2017年第7期28卷 1722-1729页

作者： Decherchi, Sergio Rocchia, Walter Ist Italiano Tecnol CONCEPT Lab I-16163 Genoa Italy BiKi Technol Srl I-16121 Genoa Italy

Recognizing the samples belonging to one class in a heterogeneous data set is a very interesting but tough machine learning task. Some samples of the data set can be actual outliers or members of other classes for which training examples are lacking. In contrast to other kernel approaches present in the literature, in this work, the problem is faced defining a one-class kernel machine that delivers the probability for a sample to belong to the support of the distribution and that can be efficiently trained by a hybrid sequential minimal optimization-expectation maximization algorithm. Due to the analogy to the import vector machine and to the one-class approach, we named the method import vector domain description (IVDD). IVDD was tested on a toy 2-D data set in order to characterize its behavior on a set of widely used benchmarking UCI data sets and, lastly, challenged against a real world outlier detection data set. All the results were compared against state-of-the-art closely related methods such as one-class-SVM and Support Vector Domain Description, proving that the algorithm is equally accurate with the additional advantage of delivering the probability estimate for each sample. Finally, a few variants aimed at providing memory savings and/ or computational speed-up in the light of big data analysis are briefly sketched.

关键词： Cognitive computation import vector machine kernel methods logistic function one-class learning outlier detection

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SYMBOLIC one-class learning FROM IMBALANCED DATASETS: APPLICATION IN MEDICAL DIAGNOSIS

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INTERNATIONAL JOURNAL ON ARTIFICIAL INTELLIGENCE TOOLS 2009年第2期18卷 273-309页

作者： Mena, Luis Gonzalez, Jesus A. Univ Zulia Fac Engn Dept Comp Sci Maracaibo 4011 Venezuela Natl Inst Astrophys Opt & Elect Dept Comp Sci Puebla Mexico

When working with real-world applications we often find imbalanced datasets, those for which there exists a majority class with normal data and a minority class with abnormal or important data. In this work, we make an overview of the class imbalance problem;we review consequences, possible causes and existing strategies to cope with the inconveniences associated to this problem. As an effort to contribute to the solution of this problem, we propose a new rule induction algorithm named Rule Extraction for MEdical Diagnosis (REMED), as a symbolic one-class learning approach. For the evaluation of the proposed method, we use different medical diagnosis datasets taking into account quantitative metrics, comprehensibility, and reliability. We performed a comparison of REMED versus C4.5 and RIPPER combined with over-sampling and cost-sensitive strategies. This empirical analysis of the REMED algorithm showed it to be quantitatively competitive with C4.5 and RIPPER in terms of the area under the Receiver Operating Characteristic curve (AUC) and the geometric mean, but overcame them in terms of comprehensibility and reliability. Results of our experiments show that REMED generated rules systems with a larger degree of abstraction and patterns closer to well-known abnormal values associated to each considered medical dataset.

关键词： Machine learning imbalanced datasets one-class learning classification algorithm rule extraction

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Towards Gas Identification in Unknown Mixtures Using an Electronic Nose with one-class learning

Towards Gas Identification in Unknown Mixtures Using an Elec...

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IEEE International Symposium on Olfaction and Electronic Nose (ISOEN)

作者： Fan, Han Jonsson, Daniel Schaffernicht, Erik Lilienthal, Achim J. Orebro Univ AASS Res Ctr Mobile Robot & Olfact Lab SE-70182 Orebro Sweden

ISBN: (纸本)9781665458603

Gas identification using an electronic nose (e-nose) typically relies on a multi-class classifier trained with extensive data of a limited set of target analytes. Usually, classification performance degrades in the presence of mixtures that include interferents not represented in the training data. This issue limits the applicability of e-noses in real-world scenarios where interferents are a priori unknown. This paper investigates the feasibility of tackling this particular gas identification problem using one-class learning. We propose several training strategies for a one-class support vector machine to deal with gas mixtures composed of a target analyte and an interferent at different concentration levels. Our evaluation indicates that accurate identification of the presence of a target analyte is achievable if it is dominant in a mixture. For interferent-dominant mixtures, extensive training is required, which implies that an improvement in the generalization ability of the one-class model is needed.

关键词： gas identification gas mixture unknown interferent one-class learning electronic nose

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A Deep one-class learning Method for Replay Attack Detection 23

A Deep One-Class Learning Method for Replay Attack Detection

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Interspeech Conference

作者： Lou, Yijie Pu, Shiliang Zhou, Jianfeng Qi, Xin Dong, Qinbo Zhou, Hongwei Hikvis Res Inst Hangzhou Peoples R China

Replay-attack is a serious issue for automatic speaker verification (ASV) and recently lots of countermeasures have been proposed to protect ASV from spoofing attacks. Traditional countermeasures are a binary-classification system which was observed to have limited generalization on unseen attacks. one-class learning methods which have been widely used in anomaly detection is a promising method to enhance the robustness of replay detection system. In this paper, we propose a deep one-class learning scheme to model the genuine speeches in a compact embedding space. To reduce the variance of genuine embedding space, we design an architecture unit, called residual variability block, which can be flexibly integrated into usual convolutional neural networks. Comprehensive experiments show that the proposed deep one-class learning scheme is effective for replay attack detection under cross-database scenarios. Besides, in our internal collected dataset, such a scheme shows better robustness under mismatched conditions between the enrollment and test phase.

关键词： one-class learning replay attack detection automatic speaker verification

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