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33rd International Conference on Scientific and Statistical Database Management (SSDBM)

作者： Feng, Jiaming Huang, Zheng Guo, Jie Qiu, Weidong Shanghai Jiao Tong Univ Shanghai Peoples R China

ISBN: (纸本)9781450384131

It is of great practical significance to accurately model and analyze abnormal events in time series. For example, the identification of anomaly patterns on infrastructure sensor curves helps locate equipment failures. In this paper, we propose an unsupervised anomaly detection approach for time series, which can comprehensively consider both point anomalies and subsequence anomalies. We innovatively introduce RNN into the architecture of adversarial autoencoder to better analyze anomaly events based on overall relationship of time series. In addition, we innovatively apply the Outlier Exposure technique for the performance optimization of anomaly detector. Meanwhile, a WGAN-based method is utilized to generate anomaly datasets through normal distribution learning. Finally, we apply the proposed method for fraud detection on a financial statement dataset and intrusion detection on a network traffic dataset. Experimental results demonstrates that our model can comprehensively consider different anomaly types in time series, and achieve promising detection performance overall. In the experiment of fraud detection, the LSTM integrated AAE model achieves an F1 score of 0.810, while the Outlier Exposure enhanced model achieves an F1 score of 0.894. This indicates that our method can improve the performance of current audit systems and facilitate discovering malicious behaviors.

关键词： anomaly detection time series adversarial autoencoder

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Black Box Explanation by Learning Image Exemplars in the Latent Feature Space

Black Box Explanation by Learning Image Exemplars in the Lat...

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European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD)

作者： Guidotti, Riccardo Monreale, Anna Matwin, Stan Pedreschi, Dino ISTI CNR Pisa Italy Univ Pisa Pisa Italy Dalhousie Univ Halifax NS Canada Polish Acad Sci Inst Comp Sci Warsaw Poland

ISBN: (纸本)9783030461508;9783030461492

We present an approach to explain the decisions of black box models for image classification. While using the black box to label images, our explanation method exploits the latent feature space learned through an adversarial autoencoder. The proposed method first generates exemplar images in the latent feature space and learns a decision tree classifier. Then, it selects and decodes exemplars respecting local decision rules. Finally, it visualizes them in a manner that shows to the user how the exemplars can be modified to either stay within their class, or to become counter-factuals by "morphing" into another class. Since we focus on black box decision systems for image classification, the explanation obtained from the exemplars also provides a saliency map highlighting the areas of the image that contribute to its classification, and areas of the image that push it into another class. We present the results of an experimental evaluation on three datasets and two black box models. Besides providing the most useful and interpretable explanations, we show that the proposed method outperforms existing explainers in terms of fidelity, relevance, coherence, and stability.

关键词： Explainable AI adversarial autoencoder Image exemplars

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Unsupervised Abstractive Text Summarization with Length Controlled autoencoder 19

Unsupervised Abstractive Text Summarization with Length Cont...

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19th IEEE-India-Council International Conference (INDICON)

作者： Dugar, Abhinav Singh, Gaurav Navyasree, B. Kumar, Anand M. Natl Inst Technol Karnataka Dept Informat Technol Surathkal 575025 India

ISBN: (纸本)9781665473507

This work deals with taking an unsupervised approach to abstractive text summarization where a large set of sentences is converted into a concise summary highlighting the essential details. This is achieved with the use of an adversarial autoencoder model. The model encodes the input to a smaller latent vector and the decoder decodes this latent code to generate the higher dimensional output with some loss. Unlike variational autoencoders, AAE's use discriminators to learn using adversarial loss. K-Means clustering and language models are used to get the final summary. This model has been tested with different datasets like the Amazon, Rotten Tomatoes and Yelp reviews dataset to essentially do an opinion summarization task and this is finally evaluated using ROGUE-1, ROGUE-2,ROGUE-L and BLEU scores. The same task is also conducted on a dataset in Hindi. We obtain a ROGUE-1 score of around 24% for Amazon, Yelp and CNN/Daily Mail dataset and a score of 12% for Rotten Tomatoes while the score obtained for the Hindi news articles dataset is only 8%.

关键词： Unsupervised Learning Abstractive Text Summarization adversarial autoencoder

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AMAD: adversarial Multiscale Anomaly Detection on High-Dimensional and Time-Evolving Categorical Data 1

AMAD: Adversarial Multiscale Anomaly Detection on High-Dimen...

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1st International Workshop on Deep Learning Practice for High-Dimensional Sparse Data with KDD (DLP KDD)

作者： Gao, Zheng Guo, Lin Ma, Chi Ma, Xiao Sun, Kai Xiang, Hang Zhu, Xiaoqiang Li, Hongsong Liu, Xiaozhong Indiana Univ Bloomington IN 47405 USA Alibaba Grp Hangzhou Peoples R China

ISBN: (纸本)9781450367837

Anomaly detection is facing with emerging challenges in many important industry domains, such as cyber security and online recommendation and advertising. The recent trend in these areas calls for anomaly detection on time-evolving data with high-dimensional categorical features without labeled samples. Also, there is an increasing demand for identifying and monitoring irregular patterns at multiple resolutions. In this work, we propose a unified end-to-end approach to solve these challenges by combining the advantages of adversarial autoencoder and Recurrent Neural Network. The model learns data representations cross different scales with attention mechanisms, on which an enhanced two-resolution anomaly detector is developed for both instances and data blocks. Extensive experiments are performed over three types of datasets to demonstrate the efficacy of our method and its superiority over the state-of-art approaches.

关键词： Anomaly Detection adversarial autoencoder High-dimensional Data

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FraudJudger: Fraud Detection on Digital Payment Platforms with Fewer Labels 21st

FraudJudger: Fraud Detection on Digital Payment Platforms wi...

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21st International Conference on Information and Communications Security (ICICS)

作者： Deng, Ruoyu Ruan, Na Zhang, Guangsheng Zhang, Xiaohu Shanghai Jiao Tong Univ Dept CSE MoE Key Lab Artificial Intelligence Shanghai Peoples R China China Telecom Bestpay Co Ltd Beijing Peoples R China

ISBN: (纸本)9783030415792;9783030415785

Automated fraud detection on electronic payment platforms is a tough problem. Fraud users often exploit the vulnerability of payment platforms and the carelessness of users to defraud money, steal passwords, do money laundering, etc., which causes enormous losses to digital payment platforms and users. There are many challenges for fraud detection in practice. Traditional fraud detection methods require a large-scale manually labeled dataset, which is hard to obtain in reality. Manually labeled data cost tremendous human efforts. In our work, we propose a semi-supervised learning detection model, FraudJudger, to analyze user behaviors on digital payment platforms and detect fraud users with fewer labeled data in training. FraudJudger can learn the latent representations of users from raw data with the help of adversarial autoencoder (AAE). Compared with other state-of-the-art fraud detection methods, FraudJudger can achieve better detection performance with only 10% labeled data. Besides, we deploy FraudJudger on a real-world financial platform, and the experiment results show that our model can well generalize to other fraud detection contexts.

关键词： Fraud detection adversarial autoencoder Semi-supervised learning

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Robust Discrimination and Generation of Faces using Compact, Disentangled Embeddings 17

Robust Discrimination and Generation of Faces using Compact,...

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IEEE/CVF International Conference on Computer Vision (ICCV)

作者： Browatzki, Bjorn Wallraven, Christian Korea Univ Seoul South Korea

ISBN: (纸本)9781728150239

Current solutions to discriminative and generative tasks in computer vision exist separately and often lack interpretability and explainability. Using faces as our application domain, here we present an architecture that is based around two core ideas that address these issues: first, our framework learns an unsupervised, low-dimensional embedding of faces using an adversarial autoencoder that is able to synthesize high-quality face images. Second, a supervised disentanglement splits the low-dimensional embedding vector into four sub-vectors, each of which contains separated information about one of four major face attributes (pose, identity, expression, and style) that can be used both for discriminative tasks and for manipulating all four attributes in an explicit manner. The resulting architecture achieves state-of-the-art image quality, good discrimination and face retrieval results on each of the four attributes, and supports various face editing tasks using a face representation of only 99 dimensions. Finally, we apply the architecture's robust image synthesis capabilities to visually debug label-quality issues in an existing face dataset.

关键词： adversarial autoencoder Disentanglement Face identification Facial expression recognition Image generation

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A DEEP FEATURE TRANSFORMATION METHOD BASED ON DIFFERENTIAL VECTOR FOR FEW-SHOT LEARNING

A DEEP FEATURE TRANSFORMATION METHOD BASED ON DIFFERENTIAL V...

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IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

作者： Guo, Qian Xu, Feng Fudan Univ Key Lab Informat Sci Electromagnet Waves MoE Shanghai 200433 Peoples R China

ISBN: (纸本)9781665403696

Due to the lack of raw data, difficulty in labeling as well as the sensor parameters limitation, few-shot learning in SAR images has become an important research direction. A deep feature transformation method based on differential vector is proposed in this paper to alleviate the feature drift problem in few-shot learning. Firstly, samples generated by a modified adversarial Auto Encoder (AAE) are introduced as an auxiliary dataset for the few-shot training dataset. Secondly, differential vector is further proposed to alleviate the cross-class-bias between the generated and real data in deep feature space. It is defined as the mean difference of the low-dimensional feature vectors of the real and generated samples, which is considered to be able to describe the transformation direction from generated to real data. Experiments conducted on MSTAR dataset demonstrate the feasibility, effectiveness and superiority of proposed method.

关键词： synthetic aperture radar few-shot learning feature drift differential vector feature transformation adversarial autoencoder

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adversarial random graph neural network for anomaly detection

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DIGITAL SIGNAL PROCESSING 2024年 146卷

作者： Tuzen, Ahmet Yaslan, Yusuf Aselsan Inc Ankara Turkiye Istanbul Tech Univ Istanbul Turkiye

Anomaly detection is distinguishing unusual objects from normal patterns. It is a complex task due to unpredictable nature of anomalies, which can appear in many forms or they can be hidden by mimicking normal behaviors in a graph structure. Such diversity makes this Deep learning approaches can solve these problems by extracting complex patterns from networks. However, addressing different forms of anomalous instances is essential for successfully implementing these approaches, as different anomaly types require further analysis. Additionally, it is challenging to interpret anomalies beforehand without focusing on every aspect of anomalies. Our objective is to propose an architecture capable of handling all types of anomalous entities by tackling challenges across various domains. In this paper, we introduce ARNAD, a novel framework that integrates three deep models to identify anomalies in graphs: graph neural network, autoencoder, and adversarial autoencoder. ARNAD approaches graph anomaly detection by utilizing the features of the deep parts, and four key elements stand out: (1) the autoencoder learns the overall graph structure and identifies highly deviated ones, (2) the graph neural network exploits graph structure to detect anomalies among the communities, (3) a fixed -size randomized neighborhood that prevents overfitting while reducing complexity (4) the adversarial autoencoder improves the robustness of the framework and discriminates anomalies. To detect anomalies, four receptive components assign risk scores to objects in the attributed network. We evaluated the framework with three synthetic datasets that simulate different behaviors of anomalies and six widely used real attributed networks. Our experimental results show that ARNAD performs competitively with other state-of-the-art models in detecting anomalous entities while minimizing false positives, demonstrating ARNAD's effectiveness in detecting graph anomalies.

关键词： Anomalous node detection Graph neural network Random neighborhood Graph autoencoder adversarial autoencoder

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DeGAN- Decomposition-based unified anomaly detection in static networks

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INFORMATION SCIENCES 2024年 677卷

作者： Tuzen, Ahmet Yaslan, Yusuf Aselsan Inc Ankara Turkiye Istanbul Tech Univ Istanbul Turkiye

Graph anomaly detection aims to identify anomalous occurrences in networks. However, this is more challenging than the traditional anomaly detection problem because anomalies in graphs can manifest in three different forms: anomalous nodes, anomalous edges, and anomalous subgraphs. It is crucial to detect all these anomaly types within a single framework to provide a unified solution to the graph anomaly detection task. The main objective of this study is to propose a model that is capable of detecting all static graph anomalies in a single architecture across various domains. In this paper, we introduce DeGAN (Decomposition -based unified Graph ANomaly detection), a novel framework for unified graph anomaly detection in static networks. DeGAN combines two deep learning concepts with graph decomposition to identify anomalous graph objects: a graph neural network and an adversarial autoencoder. DeGAN is featured with its capability to detect anomalies in a single process, and adopting graph decomposition has improved performance compared to the traditional adversarial learning approach. DeGAN is evaluated with six real -world datasets to demonstrate that our framework can work in multiple domains. Experimental results demonstrate that DeGAN is capable of detecting anomalous nodes, edges, and sub -graphs within a single model. Additionally, the effectiveness of the subcomponents of DeGAN has been demonstrated through experimentation. Even though DeGAN is proposed for plain graphs, it can be extended to attributed and dynamic graphs.

关键词： Graph anomaly detection Graph decomposition adversarial autoencoder Graph neural networks

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A Limited-View CT Reconstruction Framework Based on Hybrid Domains and Spatial Correlation

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SENSORS 2022年第4期22卷 1446-1446页

作者： Deng, Ken Sun, Chang Gong, Wuxuan Liu, Yitong Yang, Hongwen Beijing Univ Posts & Telecommun Inst Wireless Theories & Technol Lab Beijing 100876 Peoples R China

Limited-view Computed Tomography (CT) can be used to efficaciously reduce radiation dose in clinical diagnosis, it is also adopted when encountering inevitable mechanical and physical limitation in industrial inspection. Nevertheless, limited-view CT leads to severe artifacts in its imaging, which turns out to be a major issue in the low dose protocol. Thus, how to exploit the limited prior information to obtain high-quality CT images becomes a crucial issue. We notice that almost all existing methods solely focus on a single CT image while neglecting the solid fact that, the scanned objects are always highly spatially correlated. Consequently, there lies bountiful spatial information between these acquired consecutive CT images, which is still largely left to be exploited. In this paper, we propose a novel hybrid-domain structure composed of fully convolutional networks that groundbreakingly explores the three-dimensional neighborhood and works in a "coarse-to-fine" manner. We first conduct data completion in the Radon domain, and transform the obtained full-view Radon data into images through FBP. Subsequently, we employ the spatial correlation between continuous CT images to productively restore them and then refine the image texture to finally receive the ideal high-quality CT images, achieving PSNR of 40.209 and SSIM of 0.943. Besides, unlike other current limited-view CT reconstruction methods, we adopt FBP (and implement it on GPUs) instead of SART-TV to significantly accelerate the overall procedure and realize it in an end-to-end manner.

关键词： CT image reconstruction low dose protocol adversarial autoencoder deep learning hybrid domain spatial correlation inverse problems

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