What goes around comes around: Cycle-Consistency-based Short-Term Motion Prediction for Anomaly Detection using Generative Adversarial Networks

Golda, Thomas; Murzyn, Nils; Qu, Chengchao; Kroschel, Kristian

doi:10.1109/avss.2019.8909853

Cited by 6 publications

(7 citation statements)

References 16 publications

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“…Autonomy is defined as self-governance or freedom from external influences [156]. An autonomous system is referred to as a system that can perceive the environment, make decisions based on [18], [26], [29], [30], [42], [59], [78], [80], [85], [97], [98], [101], [104], [105], [123], [132]- [135], [137], [138], [141], [143] Surveillance [20]- [22], [24], [31], [37], [39], [41], [43], [54], [64], [65], [74], [75], [81], [90], [91], [109], [122] Intrusion Detection [19], [46], [76], [82], [83], [87], [93], [106], [110],…”

Section: ) Data Augmentation With Generative Adversarial Networkmentioning

confidence: 99%

“…AS CC FD HI IR ID MA ME PE SS SU SH TE TD VA San diego: [27], [36], [70] San Francisco cabspotting: [115] SBHAR: [46] SD-OCT: [17] Sentence polarity: [68] ShanghAaiTech: [41], [75] SIXray: [91] Spectralis OCT: [26] SWaT system: [93] SVHN: [50], [62] TalkingData AdTracking: [67], [113] Tennessee eastman: [16], [28] Texas coast: [27] Thyroid: [132] UBA: [96] UCI: [38], [126] UCSD: [21], [22], [37], [39], [41], [54], [64], [65], [74], [75], [79], [90], [107], [109], [122] Udacity: [56], [61] UMN: [39], [43], [64], [65], [74], [90], [107] UNSW-NB15: [110] VIRAT: [81] WADI test-bed: [93] WOA13...…”

Section: Name Of Datasetmentioning

confidence: 99%

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Applications of Generative Adversarial Networks in Anomaly Detection: A Systematic Literature Review

Sabuhi¹,

Zhou

Bezemer

et al. 2021

IEEE Access

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Anomaly detection has become an indispensable tool for modern society, applied in a wide range of applications, from detecting fraudulent transactions to malignant brain tumors. Over time, many anomaly detection techniques have been introduced. However, in general, they all suffer from the same problem: lack of data that represents anomalous behaviour. As anomalous behaviour is usually costly (or dangerous) for a system, it is difficult to gather enough data that represents such behaviour. This, in turn, makes it difficult to develop and evaluate anomaly detection techniques. Recently, generative adversarial networks (GANs) have attracted much attention in anomaly detection research, due to their unique ability to generate new data. In this paper, we present a systematic review of the literature in this area, covering 128 papers. The goal of this review paper is to analyze the relation between anomaly detection techniques and types of GANs, to identify the most common application domains for GAN-assisted and GAN-based anomaly detection, and to assemble information on datasets and performance metrics used to assess them. Our study helps researchers and practitioners to find the most suitable GAN-assisted anomaly detection technique for their application. In addition, we present a research roadmap for future studies in this area. In summary, GANs are used in anomaly detection to address the problem of insufficient amount of data for the anomalous behaviour, either through data augmentation or representation learning. The most commonly used GAN architectures are DCGANs, standard GANs, and cGANs. The primary application domains include medicine, surveillance and intrusion detection.

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Section: ) Data Augmentation With Generative Adversarial Networkmentioning

confidence: 99%

Section: Name Of Datasetmentioning

confidence: 99%

Applications of Generative Adversarial Networks in Anomaly Detection: A Systematic Literature Review

Sabuhi¹,

Zhou

Bezemer

et al. 2021

IEEE Access

View full text Add to dashboard Cite

show abstract

“…Representation Learning Normal [16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46 data. Therefore, one can learn the representation of the normal data by training GANs only on normal data.…”

Section: Task Data Type List Of Referencesmentioning

confidence: 99%

“…Precision, F1-score, accuracy, recall, sensitivity, equal error rate (EER), specificity, and receiver operating curve (ROC) are also frequently used as metrics in this area. San Francisco cabspotting: [115] SBHAR: [46] SD-OCT: [17] Sentence polarity: [68] ShanghAaiTech: [41,75] SIXray: [91] Spectralis OCT: [26] SWaT system: [93] SVHN: [50,62] TalkingData AdTracking: [113,67] Tennessee eastman: [16,28] Texas coast: [27] Thyroid: [132] UBA: [96] UCI: [38,126] UCSD: [21,22,37,39,41,54,64,65,122,74,75,79,90,107,109] Udacity: [56,61] UMN: [39,43,64,65,74,90,107] UNSW-NB15: [110] VIRAT: [81] WADI test-bed: [93] WOA13 month...…”

Section: Rq4: Which Type Of Data Instance and Datasets Are Most Commo...mentioning

confidence: 99%

“…, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105] Normal and Abnormal [106, 107, 108, 109] Representation learning with GANs Type of GAN List of references DCGAN [22, 25, 31, 32, 33, 35, 40, 44, 49, 53, 55, 60, 69, 77, 81, 86, 88, 92, 95, 97, 109, 103, 104, 105, 18, 30, 66, 78, 84, 99, 108, 96] Standard GAN[16,21,24,36,38,43,45,46,51,52,54,58,59,61,70,75,79,83,87,93,94,100,107] cGAN[23,26,37,39,56,57,64,65,72,73,74,76,85,90] …”

mentioning

confidence: 99%

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Applications of Generative Adversarial Networks in Anomaly Detection: A Systematic Literature Review

Sabuhi¹,

Zhou²,

Bezemer³

et al. 2021

Preprint

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Anomaly detection has become an indispensable tool for modern society, applied in a wide range of applications, from detecting fraudulent transactions to malignant brain tumours. Over time, many anomaly detection techniques have been introduced. However, in general, they all suffer from the same problem: a lack of data that represents anomalous behaviour. As anomalous behaviour is usually costly (or dangerous) for a system, it is difficult to gather enough data that represents such behaviour. This, in turn, makes it difficult to develop and evaluate anomaly detection techniques. Recently, generative adversarial networks (GANs) have attracted a great deal of attention in anomaly detection research, due to their unique ability to generate new data. In this paper, we present a systematic literature review of the applications of GANs in anomaly detection, covering 128 papers on the subject. The goal of this review paper is to analyze and summarize: (1) which anomaly detection techniques can benefit from certain types of GANs, and how, (2) in which application domains GAN-assisted anomaly detection techniques have been applied, and (3) which datasets and performance metrics have been used to evaluate these techniques. Our study helps researchers and practitioners to find the most suitable GAN-assisted anomaly detection technique for their application. In addition, we present a research roadmap for future studies in this area.

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Deep learning and handcrafted features for one-class anomaly detection in UAV video

Chriki

Touati

Snoussi

et al. 2020

Multimed Tools Appl

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What goes around comes around: Cycle-Consistency-based Short-Term Motion Prediction for Anomaly Detection using Generative Adversarial Networks

Cited by 6 publications

References 16 publications

Applications of Generative Adversarial Networks in Anomaly Detection: A Systematic Literature Review

Applications of Generative Adversarial Networks in Anomaly Detection: A Systematic Literature Review

Applications of Generative Adversarial Networks in Anomaly Detection: A Systematic Literature Review

Deep learning and handcrafted features for one-class anomaly detection in UAV video

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