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APRICOT: A Dataset of Physical Adversarial Attacks on Object Detection [article]

A. Braunegg, Amartya Chakraborty, Michael Krumdick, Nicole Lape, Sara Leary, Keith Manville, Elizabeth Merkhofer, Laura Strickhart, Matthew Walmer
2020 arXiv   pre-print
This dataset and the described experiments provide a benchmark for future research on the effectiveness of and defenses against physical adversarial objects in the wild.  ...  Our results suggest that adversarial patches can be effectively flagged, both in a high-knowledge, attack-specific scenario, and in an unsupervised setting where patches are detected as anomalies in natural  ...  We would also like to thank our MITRE colleagues who participated in collecting and annotating the APRICOT dataset and creating the adversarial patches.  ... 
arXiv:1912.08166v2 fatcat:b3uekiyoinf6zl2skwgbp2sgg4

ASC-Net: Unsupervised Medical Anomaly Segmentation Using an Adversarial-based Selective Cutting Network [article]

Raunak Dey, Wenbo Sun, Haibo Xu, Yi Hong
2021 arXiv   pre-print
This Adversarial-based Selective Cutting network (ASC-Net) bridges the two domains of cluster-based deep segmentation and adversarial-based anomaly/novelty detection algorithms.  ...  In this paper we consider the problem of unsupervised anomaly segmentation in medical images, which has attracted increasing attention in recent years due to the expensive pixel-level annotations from  ...  Unsupervised Anomaly Detection and Segmentation The primary school of thought in the case of unsupervised anomaly detection and segmentation has revolved around the idea of training frameworks to learn  ... 
arXiv:2112.09135v1 fatcat:gqnklo7kojbrlbz5navgn7slgq

ASC-Net : Adversarial-based Selective Network for Unsupervised Anomaly Segmentation [article]

Raunak Dey, Yi Hong
2021 arXiv   pre-print
This Adversarial-based Selective Cutting network (ASC-Net) bridges the two domains of cluster-based deep learning methods and adversarial-based anomaly/novelty detection algorithms.  ...  This concept tackles the task of unsupervised anomaly segmentation, which has attracted increasing attention in recent years due to their broad applications in tasks with unlabelled data.  ...  Furthermore, under the constraints of the GAN discriminator and the reconstruction of the original input, our ASC-Net becomes an unsupervised solution for anomaly detection, since we do not have any labels  ... 
arXiv:2103.03664v2 fatcat:3mzltezwubfqfkqv5fkqzburam

Unsupervised Learning of Anomaly Detection from Contaminated Image Data using Simultaneous Encoder Training [article]

Amanda Berg and Jörgen Ahlberg and Michael Felsberg
2019 arXiv   pre-print
Unsupervised learning of anomaly detection in high-dimensional data, such as images, is a challenging problem recently subject to intense research.  ...  In this work, we evaluate the effects of anomaly contaminations in the training data on state-of-the-art GAN-based anomaly detection methods. As expected, detection performance deteriorates.  ...  In the context of machine learning, anomaly detection can be supervised, semi-supervised, or unsupervised. This paper addresses unsupervised anomaly detection.  ... 
arXiv:1905.11034v2 fatcat:ghyb2wbiinbfvprc2nucbqlv5e

Unsupervised Anomaly Detection with Adversarial Mirrored AutoEncoders [article]

Gowthami Somepalli, Yexin Wu, Yogesh Balaji, Bhanukiran Vinzamuri, Soheil Feizi
2021 arXiv   pre-print
Detecting out of distribution (OOD) samples is of paramount importance in all Machine Learning applications.  ...  Our method outperforms the current state-of-the-art methods for anomaly detection on several OOD detection benchmarks.  ...  This project was supported in part by NSF CAREER AWARD 1942230, an IBM faculty award, a grant from Capital One, and a Simons Fellowship on Deep Learning Foundations.  ... 
arXiv:2003.10713v3 fatcat:rhff735uxrborke4rwisjs6wm4

Effective Detection of Multimedia Protocol Tunneling using Machine Learning

Diogo Barradas, Nuno Santos, Luís E. T. Rodrigues
2018 USENIX Security Symposium  
We also explore the application of semi-supervised and unsupervised machine learning techniques.  ...  Our findings suggest that the existence of manually labeled samples is a requirement for the successful detection of covert channels.  ...  Our work further explored the application of semi-supervised and unsupervised anomaly detection techniques in the same context.  ... 
dblp:conf/uss/Barradas0R18 fatcat:zcoll5ywurc43kobupk3spwphy

PORTFILER: Port-Level Network Profiling for Self-Propagating Malware Detection [article]

Talha Ongun, Oliver Spohngellert, Benjamin Miller, Simona Boboila, Alina Oprea, Tina Eliassi-Rad, Jason Hiser, Alastair Nottingham, Jack Davidson, Malathi Veeraraghavan
2022 arXiv   pre-print
PORTFILER extracts port-level features from the Zeek connection logs collected at a border of a monitored network, applies anomaly detection techniques to identify suspicious events, and ranks the alerts  ...  It is challenging to detect these attacks in their early stages, as adversaries utilize common network services, use novel techniques, and can evade existing detection mechanisms.  ...  ACKNOWLEDGEMENTS This research was sponsored by the contract number W911NF-18-C0019 with the U.S.  ... 
arXiv:2112.13798v2 fatcat:4exe4kpfdfhypjyxbkcv6iba5u

Anomaly Detection Using GANs for Visual Inspection in Noisy Training Data [article]

Masanari Kimura, Takashi Yanagihara
2018 arXiv   pre-print
In recent years, due to the difficulty of defining anomalies and the limit of correcting their labels, research on unsupervised anomaly detection using generative models has attracted attention.  ...  The detection and the quantification of anomalies in image data are critical tasks in industrial scenes such as detecting micro scratches on product.  ...  In recent years, research on unsupervised anomaly detection using generative models has attracted attention.  ... 
arXiv:1807.01136v2 fatcat:plzffmummvbunpqjnh5mwpfqxu

A Decade of Social Bot Detection [article]

Stefano Cresci
2020 arXiv   pre-print
In this work, we briefly survey the first decade of research in social bot detection.  ...  In the aftermath of the 2016 US elections, the world started to realize the gravity of widespread deception in social media.  ...  social platforms -and the ability of humans in spotting bots in-the-wild.  ... 
arXiv:2007.03604v2 fatcat:6dilhijyzzgnhmlrl5ly2vknue

MimicGAN: Robust Projection onto Image Manifolds with Corruption Mimicking [article]

Rushil Anirudh, Jayaraman J. Thiagarajan, Bhavya Kailkhura, Timo Bremer
2020 arXiv   pre-print
More importantly, we show that our approach produces state-of-the-art performance in several GAN-based applications -- anomaly detection, domain adaptation, and adversarial defense, that benefit from an  ...  As a result, they have been widely adopted across a variety of applications, ranging from challenging inverse problems like image completion, to problems such as anomaly detection and adversarial defense  ...  Anomaly Detection GANs have become a popular choice for unsupervised anomaly detection since out of distribution samples are represented as those samples with a relatively high reprojection error, since  ... 
arXiv:1912.07748v3 fatcat:danggesoinburbth2pixzj74pm

Semi-Supervised Anomaly Detection in Video-Surveillance Scenes in the Wild

Mohammad Ibrahim Sarker, Cristina Losada-Gutiérrez, Marta Marrón-Romera, David Fuentes-Jiménez, Sara Luengo-Sánchez
2021 Sensors  
in anomaly detection in all of them.  ...  In this work, it is proposed an approach for anomaly detection in video-surveillance scenes based on a weakly supervised learning algorithm.  ...  Finally, in the context of unsupervised reconstruction-based models, also sparse combination learning techniques have been adopted in [12] to detect anomalies in surveillance contexts.  ... 
doi:10.3390/s21123993 pmid:34207883 pmcid:PMC8230050 fatcat:kvxd3ujt3rfohf5qg4s3eb4hzm

SIGL: Securing Software Installations Through Deep Graph Learning [article]

Xueyuan Han, Xiao Yu, Thomas Pasquier, Ding Li, Junghwan Rhee, James Mickens, Margo Seltzer, Haifeng Chen
2021 arXiv   pre-print
and adversarial attack.  ...  It can be used with application-specific models, even in the presence of new software versions, as well as application-agnostic meta-models that encompass a wide range of applications and installers.  ...  The views, opinions, and/or findings contained in this paper are those of the authors and should not be interpreted as representing the official views or policies, either expressed or implied, of the sponsors  ... 
arXiv:2008.11533v2 fatcat:dqdb6itehjbhpesqq6pn56wasq

Deep Generative Models in the Industrial Internet of Things: A Survey

Suparna De, Maria Bermudez-Edo, Honghui Xu, Zhipeng Cai
2022 IEEE Transactions on Industrial Informatics  
In this article, we review the state of the art of DGMs and their applicability to IIoT, classifying the reviewed works into the IIoT application areas of anomaly detection, trust-boundary protection,  ...  Advances in communication technologies and artificial intelligence are accelerating the paradigm of industrial Internet of Things (IIoT).  ...  databases and automatically learns the rapidly changing natures of unknown attack models by using unsupervised learning and unlabeled data from the wild.  ... 
doi:10.1109/tii.2022.3155656 fatcat:5np4ghh43nfqjkjycrzkle6vlu

A new interpretable unsupervised anomaly detection method based on residual explanation

David F. N. Oliveira, Lucio F. Vismari, Alexandre M. Nascimento, Jorge R. De Almeida, Paulo S. Cugnasca, Joao B. Camargo, Leandro Almeida, Rafael Gripp, Marcelo Neves
2021 IEEE Access  
Although a current hot topic, further advances are still needed to overcome the existing limitations of the current interpretability methods in unsupervised DL-based models for Anomaly Detection (AD).  ...  Despite the superior performance in modeling complex patterns to address challenging problems, the black-box nature of Deep Learning (DL) methods impose limitations to their application in real-world critical  ...  ACKNOWLEDGMENTS The authors would like to thank VALE S.A. for the institutional support that made this work possible.  ... 
doi:10.1109/access.2021.3137633 fatcat:4ctyo2norbhahioqkuc2xj74je

Simple and Effective Prevention of Mode Collapse in Deep One-Class Classification [article]

Penny Chong, Lukas Ruff, Marius Kloft, Alexander Binder
2020 arXiv   pre-print
Anomaly detection algorithms find extensive use in various fields. This area of research has recently made great advances thanks to deep learning.  ...  The method has shown promising results in both unsupervised and semi-supervised settings.  ...  To detect new anomalies, many of these systems adopt an unsupervised learning approach as foundation for their detection algorithm.  ... 
arXiv:2001.08873v3 fatcat:bzxna4rtubb7pcv7qk24clfbji
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