Relevant bibliographies by topics / Deep Fingerprinting

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Deep Fingerprinting'

Author: Grafiati
Published: 29 June 2021
Last updated: 18 February 2022

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Journal articles on the topic "Deep Fingerprinting"

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Guo, Maohua, Jinlong Fei, and Yitong Meng. "Deep Nearest Neighbor Website Fingerprinting Attack Technology." Security and Communication Networks 2021 (July 14, 2021): 1–14. http://dx.doi.org/10.1155/2021/5399816.

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By website fingerprinting (WF) technologies, local listeners are enabled to track the specific website visited by users through an investigation of the encrypted traffic between the users and the Tor network entry node. The current triplet fingerprinting (TF) technique proved the possibility of small sample WF attacks. Previous research methods only concentrate on extracting the overall features of website traffic while ignoring the importance of website local fingerprinting characteristics for small sample WF attacks. Thus, in the present paper, a deep nearest neighbor website fingerprinting (DNNF) attack technology is proposed. The deep local fingerprinting features of websites are extracted via the convolutional neural network (CNN), and then the k-nearest neighbor (k-NN) classifier is utilized to classify the prediction. When the website provides only 20 samples, the accuracy can reach 96.2%. We also found that the DNNF method acts well compared to the traditional methods in coping with transfer learning and concept drift problems. In comparison to the TF method, the classification accuracy of the proposed method is improved by 2%–5% and it is only dropped by 3% when classifying the data collected from the same website after two months. These experiments revealed that the DNNF is a more flexible, efficient, and robust website fingerprinting attack technology, and the local fingerprinting features of websites are particularly important for small sample WF attacks.
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Song, Pingfan, Yonina C. Eldar, Gal Mazor, and Miguel R. D. Rodrigues. "HYDRA: Hybrid deep magnetic resonance fingerprinting." Medical Physics 46, no. 11 (September 10, 2019): 4951–69. http://dx.doi.org/10.1002/mp.13727.

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Cohen, Ouri, Bo Zhu, and Matthew S. Rosen. "MR fingerprinting Deep RecOnstruction NEtwork (DRONE)." Magnetic Resonance in Medicine 80, no. 3 (April 6, 2018): 885–94. http://dx.doi.org/10.1002/mrm.27198.

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Oh, Se Eun, Saikrishna Sunkam, and Nicholas Hopper. "p1-FP: Extraction, Classification, and Prediction of Website Fingerprints with Deep Learning." Proceedings on Privacy Enhancing Technologies 2019, no. 3 (July 1, 2019): 191–209. http://dx.doi.org/10.2478/popets-2019-0043.

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Abstract Recent advances in Deep Neural Network (DNN) architectures have received a great deal of attention due to their ability to outperform state-of-the-art machine learning techniques across a wide range of application, as well as automating the feature engineering process. In this paper, we broadly study the applicability of deep learning to website fingerprinting. First, we show that unsupervised DNNs can generate lowdimensional informative features that improve the performance of state-of-the-art website fingerprinting attacks. Second, when used as classifiers, we show that they can exceed performance of existing attacks across a range of application scenarios, including fingerprinting Tor website traces, fingerprinting search engine queries over Tor, defeating fingerprinting defenses, and fingerprinting TLS-encrypted websites. Finally, we investigate which site-level features of a website influence its fingerprintability by DNNs.
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Oh, Se Eun, Nate Mathews, Mohammad Saidur Rahman, Matthew Wright, and Nicholas Hopper. "GANDaLF: GAN for Data-Limited Fingerprinting." Proceedings on Privacy Enhancing Technologies 2021, no. 2 (January 29, 2021): 305–22. http://dx.doi.org/10.2478/popets-2021-0029.

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Abstract We introduce Generative Adversarial Networks for Data-Limited Fingerprinting (GANDaLF), a new deep-learning-based technique to perform Website Fingerprinting (WF) on Tor traffic. In contrast to most earlier work on deep-learning for WF, GANDaLF is intended to work with few training samples, and achieves this goal through the use of a Generative Adversarial Network to generate a large set of “fake” data that helps to train a deep neural network in distinguishing between classes of actual training data. We evaluate GANDaLF in low-data scenarios including as few as 10 training instances per site, and in multiple settings, including fingerprinting of website index pages and fingerprinting of non-index pages within a site. GANDaLF achieves closed-world accuracy of 87% with just 20 instances per site (and 100 sites) in standard WF settings. In particular, GANDaLF can outperform Var-CNN and Triplet Fingerprinting (TF) across all settings in subpage fingerprinting. For example, GANDaLF outperforms TF by a 29% margin and Var-CNN by 38% for training sets using 20 instances per site.
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Zhang, Qiang, Pan Su, Zhensen Chen, Ying Liao, Shuo Chen, Rui Guo, Haikun Qi, et al. "Deep learning–based MR fingerprinting ASL ReconStruction (DeepMARS)." Magnetic Resonance in Medicine 84, no. 2 (February 4, 2020): 1024–34. http://dx.doi.org/10.1002/mrm.28166.

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Zhao, Jingjing, Qingyue Hu, Gaoyang Liu, Xiaoqiang Ma, Fei Chen, and Mohammad Mehedi Hassan. "AFA: Adversarial fingerprinting authentication for deep neural networks." Computer Communications 150 (January 2020): 488–97. http://dx.doi.org/10.1016/j.comcom.2019.12.016.

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Lee, Woongsup, Seon Yeob Baek, and Seong Hwan Kim. "Deep-Learning-Aided RF Fingerprinting for NFC Security." IEEE Communications Magazine 59, no. 5 (May 2021): 96–101. http://dx.doi.org/10.1109/mcom.001.2000912.

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Li, Tao, Hai Wang, Yuan Shao, and Qiang Niu. "Channel state information–based multi-level fingerprinting for indoor localization with deep learning." International Journal of Distributed Sensor Networks 14, no. 10 (October 2018): 155014771880671. http://dx.doi.org/10.1177/1550147718806719.

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With the rapid growth of indoor positioning requirements without equipment and the convenience of channel state information acquisition, the research on indoor fingerprint positioning based on channel state information is increasingly valued. In this article, a multi-level fingerprinting approach is proposed, which is composed of two-level methods: the first layer is achieved by deep learning and the second layer is implemented by the optimal subcarriers filtering method. This method using channel state information is termed multi-level fingerprinting with deep learning. Deep neural networks are applied in the deep learning of the first layer of multi-level fingerprinting with deep learning, which includes two phases: an offline training phase and an online localization phase. In the offline training phase, deep neural networks are used to train the optimal weights. In the online localization phase, the top five closest positions to the location position are obtained through forward propagation. The second layer optimizes the results of the first layer through the optimal subcarriers filtering method. Under the accuracy of 0.6 m, the positioning accuracy of two common environments has reached, respectively, 96% and 93.9%. The evaluation results show that the positioning accuracy of this method is better than the method based on received signal strength, and it is better than the support vector machine method, which is also slightly improved compared with the deep learning method.
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Fang, Zhenghan, Yong Chen, Sheng‐Che Hung, Xiaoxia Zhang, Weili Lin, and Dinggang Shen. "Submillimeter MR fingerprinting using deep learning–based tissue quantification." Magnetic Resonance in Medicine 84, no. 2 (December 19, 2019): 579–91. http://dx.doi.org/10.1002/mrm.28136.

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Dissertations / Theses on the topic "Deep Fingerprinting"

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Magnusson, Jonathan. "Evaluation of a Proposed Traffic-Splitting Defence for Tor : Using Directional Time and Simulation Against TrafficSliver." Thesis, Karlstads universitet, Institutionen för matematik och datavetenskap (from 2013), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kau:diva-84762.

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Tor is a Privacy-Enhancing Technology based on onion routing which lets its users browse the web anonymously. Even though the traffic is encrypted in multiple layers, traffic analysis can still be used to gather information from meta-data such as time, size, and direction of the traffic. A Website Fingerprinting (WF) attack is characterized by monitoring traffic locally to the user in order to predict the destination website based on the observed patterns. TrafficSliver is a proposed defence against WF attacks which splits the traffic on multiple paths in the Tor network. This way, a local attacker is assumed to only be able to observe a subset of all the user's total traffic. The initial evaluation of TrafficSliver against Deep Fingerprinting (DF), the state-of-the-art WF attack, showed promising results for the defence, reducing the accuracy of DF from over 98% down to less than 7% without adding artificial delays or dummy traffic. In this thesis, we further evaluate TrafficSliver against DF beyond what was done in the original work by De la Cadena et al. by using a richer data representation and finding out whether it is possible to utilize simulated training data to improve the accuracy of the attack. By introducing directional time as a richer data representation and increasing the size of the training dataset using a simulator, the accuracy of DF was improved against TrafficSliver on three different datasets. Against the original dataset provided by the authors of TrafficSliver, the accuracy was initially 7.1% and then improved to 49.9%. The results were confirmed by using two additional datasets with TrafficSliver, where the accuracy was improved from 5.4% to 44.9% and from 9.8% to 37.7%.
Tor är ett personlig-integritetsverktyg baserat på onion routing som låter sina användare anonymnt besöka hemsidor på internet. Även om trafiken är enkrypterad i flera lager, kan trafikanalys användas för att utvinna information från metadata som exempelvis: tid, storlek och riktning av trafik. En Website Fingerprinting (WF)-attack karaktäriseras av att övervaka trafik nära användaren för att sedan avgöra vilken hemsida som besökts utifrån mönster. TrafficSliver är ett föreslaget försvar mot WF-attacker genom att dela upp trafiken på flera vägar genom nätverket. Detta gör att en attackerare antas endast kunna se en delmängd av användarens totala trafik. Den första utvärderingen av TrafficSliver mot Deep Fingerprinting (DF), spjutspetsen inom WF-attacker, visade lovande resultat för försvaret genom att reducera träffsäkerheten av DF från över 98% till mindre än 7% utan att lägga till artificiella fördröjningar eller falsk trafik. I denna uppsats strävar vi att fortsätta utvärderingen av TrafficSliver mot DF utöver vad som redan har gjorts av De la Cadena et al. med en rikare datarepresentation och en undersökning huruvida det går att använda simulerad data för att träna attacker mot försvaret. Genom att introducera riktad tid och öka mängden data för att träna attacken, ökades träffsäkerheten av DF mot TrafficSliver på tre distinkta dataset. Mot det dataset som samlades in av TrafficSliver var träffsäkerheten inledelsevis 7.1% och sedan förbättrad med hjälp av riktad tid och större mängder av simulerad träningsdata till 49.9%. Dessa resultat bekräftades även för två ytterligare dataset med TrafficSliver, där träffsäkerheten blev förbättrad från 5.4% till 44.9% och från 9.8% till 37.7%.
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Williams, Thomas. "Investigating the circulation of Southern Ocean deep water masses over the last 1.5 million years by geochemical fingerprinting of marine sediments." Thesis, University of Cambridge, 2018. https://www.repository.cam.ac.uk/handle/1810/274006.

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The Southern Ocean (SO) is a critical component in the global ocean conveyor. As the only conduit linking the Atlantic, Indian and Pacific Oceans, as well as an important region of upwelling and water mass formation, it is thought to have played a key role in modulating Earth’s past climate. Changes in the circulation of SO deep and bottom waters over the last 1.5 million years are investigated using stable carbon isotope $δ^{13}C$ measurements made on the tests of the benthic foraminfer Cibicidoides ($δ^{13}C_{b}$), and the rare earth element concentrations and Neodymium isotope ($ɛ_{Nd}$) values of marine sediments and their authigenic ferromanganese coatings. Being a proxy for past seawater nutrient contents, $δ^{13}C_{b}$ provides important insights into both past ocean circulation and the potential storage of remineralised organic carbon within the deep ocean, while simultaneously providing information on the past ventilation state of the deep ocean interior. As seawater $ɛ_{Nd}$ remains unaffected by biological fractionation or air-sea exchange processes, reconstructions of past deep and bottom water $ɛ_{Nd}$ provides a tool with which to study past changes in the circulation and mixing of these water masses. A suite of previously published late Holocene (0-6 ka) and Last Glacial Maximum (LGM; 18-24 ka) $δ^{13}C_{b}$ data are used alongside newly acquired $δ^{13}C_{b}$ data from the Amundsen Sea in the eastern Pacific sector of the SO to investigate past changes in the pattern of circum-Antarctic seawater carbon isotope composition. The $δ^{13}C$ signature of deep and bottom waters was much more heterogenous during the LGM than the late Holocene, with negative $δ^{13}C$ excursions occurring within the Atlantic and Indian sectors of the SO below c. 2-3 km water depth. Some of this negative $δ^{13}C$ signal was advected through the SO to the Pacific sector, but this appears to have been restricted by bathymetric barriers within the SO. New $δ^{13}C_{b}$ data spanning the last 800 ka from the Amundsen Sea are presented and suggest differing modes of bottom water formation in the Atlantic vs Pacific sectors of the SO during glacial periods of the last 800 ka. An authigenic $ɛ_{Nd}$ record measured on sediments from a core located in the deep Indian Ocean is used to investigate the palaeocirculation history of modified Circumpolar Deep Water (mCDW) within the Indian Ocean during the last 1.5 million years. Shifts towards more radiogenic $ɛ_{Nd}$ values during glacial periods are interpreted as reflecting a decreased entrainment of deep waters sourced in the North Atlantic (Northern Component Water, NCW) within CDW, which led to a reduced advection of an unradiogenic $ɛ_{Nd}$ NCW signal to the core site. $ɛ_{Nd}$ and REE measurements made on sediments from two cores located on the Pacific-Antarctic Ridge in the western Pacific sector of the SO (to the north of the Ross Sea Embayment) are used to reconstruct the bottom water palaeocirculation in this region across the last 540 ka. The proportion and $ɛ_{Nd}$ signature of Ross Sea Bottom Water (RSBW) bathing these core sites has fluctuated throughout the last 540 ka. These fluctuations suggest the rate and location of bottom water formation within the Ross Sea, and the supply of terrigenous material with radiogenic $ɛ_{Nd}$ values with which to isotopically `labelled' RSBW, may have changed in the past.
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Williams, Timothy Roy. "Reeves' muntjac : a molecular genetic study of an invading species." Thesis, University of Kent, 1994. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.283343.

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TAREKEGN, GETANEH BERIE, and Getaneh Berie Tarekegn. "DFOPS: Fingerprinting Outdoor Positioning Scheme in Hybrid Networks: A Deep Learning Approach." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/9jzm29.

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碩士
國立臺北科技大學
電資國際專班
107
Recently, Location Based Services (LBSs) are becoming a key technology for enhancing the applicability of Internet-of-Things (IoT) to offer seamless, intelligent and adaptive services in academia and industry to create smart world due to the growth of multiple built-in sensors on mobile devices and wireless technology. Satellite-based positioning (e.g., GPS) do not work well for urban and suburban outdoor positioning for the success of IoT deployment because of Line of Sight (LoS) problems and it requiring much power. Besides, most satellite-based positioning systems are not cost-effective. Hence accurate and efficient positioning services is a major challenge in urban and suburban environments. In this paper, we propose an accurate, cost-efficient and robust Fingerprinting Outdoor Positioning Scheme(DFOPS) in a scalable environment using hybrid Support Vector Machine (SVM) and Long Short-Term Memory (LSTM) algorithms. DFOPS contains Wi-Fi and Orthogonal Frequency Division Multiplexing (OFDM) signal values from each reachable Wi-Fi Access Points (APs) and three temporary deployed Unmanned Arial Vehicles Base Stations (UAV-BSs), respectively, to construct a radio-map. Due to signal unreachable and shadowing effects, the signal values are missed. Accordingly, we fill the missing values of the radio-map using mean of RSS values at each reference point. Since we collect multiple RSS measurements at each reference points over time with different mobile devices to mitigate signal variations during constructing a radio-map. We apply Linear Discriminant Analysis (LDA) to remove outliers, extract unique features from raw data, reduce Wi-Fi and OFDM features, speed up learning processes, and optimize the proposed algorithm performances. The proposed hybrid SVM+LSTM model (i.e., DFOPS) is the sequential combination of classification and regression model, which is SVM with Radial Basis Function (RBF) kernel as classifier and LSTM as regression model. In this model, to limit the search space and improve the positioning performance, we primarily apply SVM classifier as a coarse positioning that predicts the class of a mobile user inferred from the class-based signal distribution. Secondly, by adding the SVM predicted class of the mobile user as one feature to the signal reading of the target IoT device, we use an input to the LSTM model. Then, the LSTM model is used to figure out the current positioning (latitude, longitude) of the mobile user. The proposed system is evaluated on experimental datasets in real environment in three different scenarios. To assess the trustworthiness of the SVM classifier, we compare with k-Nearest Neighbor(k-NN), Random Forest, and Multilayer Perceptron (MLP) algorithms in all our proposed scenarios. The experimental result shows that SVM has better positioning performance in all scenarios than the others. The proposed DFOPS model achieved positioning errors less than 1.5 m are 87.46%, 92.74% and 99.23% for Scenario-I (original collected Wi-Fi and OFDM signal), Scenario-II (reduced the Scenario-I signal features using PCA) and Scenario-III (reduced the Scenario-I signal features using LDA), respectively. The model achieved no more than 1.29 m position estimation errors. This shows that the proposed model achieves a promising and reasonable positioning services of the IoT devices in wireless environment.
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Yang, Jia-Hong, and 楊佳虹. "A Robust Music Auto-Tagging Technique Using Audio Fingerprinting and Deep Convolutional Neural Networks." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/vagbse.

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碩士
國立中興大學
資訊科學與工程學系
106
Music tags are a set of descriptive keywords that convey high-level information about a music clip, such as emotions(sadness, happiness), genres(jazz, classical), and instruments(guitar, vocal). Since tags provide high-level information from the listener’s perspectives, they can be used for music discovery and recommendation. However, in music information retrieval (MIR), researchers need to have expertise based on acoustics or engineering design in order to analyze and organize music informations, classify them according to music forms, and then provide music information retrieval. In recent years, people have been paying more attention to the feature learning and deep architecture, thus reducing the required of the engineering works and the need for prior knowledge. The use of deep convolutional neural networks has been successfully explored in the image, text and speech field. However, previous methods for music auto-tagging can’t accurately discriminate the type of music for the distortion and noise audio, it will have the bad results in the auto-tagging. Therefore, we will propose a robust method to implement auto-music tagging. First, convert the music into a spectrogram, and find out the important information from the spectrogram, that is, the audio fingerprint. Then use it as the input of convolutional neural networks to learn the features, in this way to get a good music search result. Experimental results demonstrate the robustness of the proposed method.
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Books on the topic "Deep Fingerprinting"

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Brinkman, Todd J. Assessing population trends of deer in southeast Alaska using a DNA-based approach: A general guide, version 1.0. Juneau, AK: Alaska Dept. of Fish and Game, Division of Wildlife Conservation, 2010.

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Brinkman, Todd J. Using DNA to test the utility of pellet-group counts as indices of deer density. Juneau, AK: Alaska Dept. of Fish and Game, Division of Wildlife Conservation, 2010.

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Book chapters on the topic "Deep Fingerprinting"

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Billah Karbab, ElMouatez, Mourad Debbabi, Abdelouahid Derhab, and Djedjiga Mouheb. "Portable Supervised Malware Fingerprinting Using Deep Learning." In Android Malware Detection using Machine Learning, 143–64. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-74664-3_7.

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Abid, Mahdi, and Grégoire Lefebvre. "Deep Neural Networks for Indoor Geomagnetic Field Fingerprinting with Regression Approach." In Proceedings of the International Neural Networks Society, 178–89. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-80568-5_15.

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Wang, Jiankun, Zenghua Zhao, Jiayang Cui, Yu Wang, YiYao Shi, and Bin Wu. "Low-Cost Wi-Fi Fingerprinting Indoor Localization via Generative Deep Learning." In Wireless Algorithms, Systems, and Applications, 53–64. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-85928-2_5.

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van Schooten, Kipp. "Towards Chemical Fingerprinting of Deep-Level Defect Sites in CdS Nanocrystals by Optically Detected Spin Coherence." In Optically Active Charge Traps and Chemical Defects in Semiconducting Nanocrystals Probed by Pulsed Optically Detected Magnetic Resonance, 75–88. Heidelberg: Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-00590-4_4.

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Fang, Zhenghan, Yong Chen, Mingxia Liu, Yiqiang Zhan, Weili Lin, and Dinggang Shen. "Deep Learning for Fast and Spatially-Constrained Tissue Quantification from Highly-Undersampled Data in Magnetic Resonance Fingerprinting (MRF)." In Machine Learning in Medical Imaging, 398–405. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-00919-9_46.

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Fang, Zhenghan, Yong Chen, Mingxia Liu, Yiqiang Zhan, Weili Lin, and Dinggang Shen. "Correction to: Deep Learning for Fast and Spatially-Constrained Tissue Quantification from Highly-Undersampled Data in Magnetic Resonance Fingerprinting (MRF)." In Machine Learning in Medical Imaging, C1. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-00919-9_47.

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Dada, Michael O., and Bamidele O. Awojoyogbe. "A Computational MRI Based on Bloch’s NMR Flow Equation, MRI Fingerprinting and Python Deep Learning for Classifying Adult Brain Tumors." In Biological and Medical Physics, Biomedical Engineering, 179–217. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-76728-0_5.

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Bhuyan, Manasjyoti, and Kandarpa Kumar Sarma. "Fingerprinting Based Localization with Channel State Information Features and Spatio-Temporal Deep Learning in Long Term Evolution Femtocell Network: An Experimental Approach." In Sustainable Communication Networks and Application, 156–63. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-34515-0_16.

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Buckley, Michael, Richard G. Cooke, María Fernanda Martínez, Fernando Bustamante, Máximo Jiménez, Alexandra Lara, and Juan Guillermo Martín. "Archaeological Collagen Fingerprinting in the Neotropics; Protein Survival in 6000 Year Old Dwarf Deer Remains from Pedro González Island, Pearl Islands, Panama." In Zooarchaeology in the Neotropics, 157–75. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-57328-1_10.

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Conference papers on the topic "Deep Fingerprinting"

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Sirinam, Payap, Mohsen Imani, Marc Juarez, and Matthew Wright. "Deep Fingerprinting." In CCS '18: 2018 ACM SIGSAC Conference on Computer and Communications Security. New York, NY, USA: ACM, 2018. http://dx.doi.org/10.1145/3243734.3243768.

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Nicolussi, Alessandro, Simon Tanner, and Roger Wattenhofer. "Aircraft Fingerprinting Using Deep Learning." In 2020 28th European Signal Processing Conference (EUSIPCO). IEEE, 2021. http://dx.doi.org/10.23919/eusipco47968.2020.9287691.

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Jafari, Hossein, Oluwaseyi Omotere, Damilola Adesina, Hsiang-Huang Wu, and Lijun Qian. "IoT Devices Fingerprinting Using Deep Learning." In MILCOM 2018 - IEEE Military Communications Conference. IEEE, 2018. http://dx.doi.org/10.1109/milcom.2018.8599826.

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Rimmer, Vera, Davy Preuveneers, Marc Juarez, Tom Van Goethem, and Wouter Joosen. "Automated Website Fingerprinting through Deep Learning." In Network and Distributed System Security Symposium. Reston, VA: Internet Society, 2018. http://dx.doi.org/10.14722/ndss.2018.23105.

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Xu, Tongyang, and Izzat Darwazeh. "Faster URLLC: Deep Learning Waveform Fingerprinting." In 2021 IEEE International Conference on Communications Workshops (ICC Workshops). IEEE, 2021. http://dx.doi.org/10.1109/iccworkshops50388.2021.9473878.

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He, Zecheng, Tianwei Zhang, and Ruby Lee. "Sensitive-Sample Fingerprinting of Deep Neural Networks." In 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2019. http://dx.doi.org/10.1109/cvpr.2019.00486.

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Seto, Mae L., and Parmeet Singh. "Visual fingerprinting for lobsters using deep learning." In 2019 IEEE International Conference on Systems, Man and Cybernetics (SMC). IEEE, 2019. http://dx.doi.org/10.1109/smc.2019.8914029.

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Cui, Weiqi, Tao Chen, and Eric Chan-Tin. "More Realistic Website Fingerprinting Using Deep Learning." In 2020 IEEE 40th International Conference on Distributed Computing Systems (ICDCS). IEEE, 2020. http://dx.doi.org/10.1109/icdcs47774.2020.00058.

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Wang, Si, and Chip-Hong Chang. "Fingerprinting Deep Neural Networks - a DeepFool Approach." In 2021 IEEE International Symposium on Circuits and Systems (ISCAS). IEEE, 2021. http://dx.doi.org/10.1109/iscas51556.2021.9401119.

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Seok, Keun Young, and Jung Hoon Lee. "Deep Learning Based Fingerprinting Scheme for Wireless Positioning." In 2020 International Conference on Artificial Intelligence in Information and Communication (ICAIIC). IEEE, 2020. http://dx.doi.org/10.1109/icaiic48513.2020.9065054.

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