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5 results for “Presentation Attack”
GPS Location Spoofing Attack Detection for Enhancing the Security of Autonomous Vehicles (presentation video)
<p>Video of the presentation for the publication M. Kamal, A. Barua, C. Vitale, C. Laoudias and G. Ellinas, "GPS Location Spoofing Attack Detection for Enhancing the Security of Autonomous Vehicles," 2021 IEEE 94th Vehicular Technology Conference (VTC2021-Fall), 2021, pp. 1-7, doi: 10.1109/VTC2021-Fall52928.2021.9625567.</p>
A Smartphone APPlication for the Transmission of ECGs in the Management of Patients Presenting With Suspected Heart Attacks in the Hamilton, Niagara, Haldimand, and Brant Area
ClinicalTrials.gov study NCT05290389. IPD Sharing: NO. Countries: 1. Publications: 1.
Clinical, Airway Inflammatory, and HRA Phenotypes, in Preschool Children With Acute Asthmatic Attack Presenting to the ED.
ClinicalTrials.gov study NCT01175174. IPD Sharing: Not stated. Countries: 1. Publications: 0.
RECOD Mobile Presentation-Attack Dataset (RECOD-MPAD)
<p>=============<br> Introduction</p> <p>The RECOD Mobile Presentation-Attack Dataset (RECOD-MPAD) is intended for the study of presentation attacks (PAs, also known as spoof attempts) to facial recognition systems in mobile devices.<br> It consists of frames depicting genuine attempts of unlocking a smartphone, as well as two types of presentation attacks: using printouts of the user face; or using electronic displays showing the user's face.</p> <p>More details can be found in the accompanying paper:<br> <em>Detecting face presentation attacks in mobile devices with a patch-based CNN and a sensor-aware loss function</em><br> Waldir R. Almeida, Fernanda A. Andaló, Rafael Padilha, Gabriel Bertocco, William Dias, Ricardo da S. Torres, Jacques Wainer, Anderson Rocha<br> PLoS ONE, 2020 (https://doi.org/10.1371/journal.pone.0238058)<br> <br> This dataset was developed as part of a research project at the RECOD lab of the Institute of Computing, University of Campinas, Brazil.</p> <p><br> ==========<br> Statistics</p> <p>Number of users: 45<br> Men: 30/45<br> Glasses: 14/45<br> Beard: 13/45<br> Age range: 18-50</p> <p><br> =============================<br> Basic metadata and file names</p> <p>Name format:<br> <device>_<session>_<user>_<label>_<frame></p> <p>Label: <br> 0: real, genuine access attempt<br> 1: printout attack - recaptured indoors<br> 2: printout attack - recaptured outdoors (more light)<br> 3: screen attack - large display (CCE TV)<br> 4: screen attack - medium-sized display (HP monitor)</p> <p>Device:<br> 1: motog3<br> 2: xt1572</p> <p>Session:<br> 1: outdoors natural direct light<br> 2: outdoors natural diffuse light/shadow<br> 3: indoors, top main light<br> 4: indoors, side light (sunlight coming through window or door)<br> 5: indoors, low-light / noisy</p> <p>Example:<br> 1_5_20_00_0050</p> <p><br> ======================<br> Additional information</p> <p>In each sequence, the volunteers followed the same instructions:<br> - Hold the phone as if using it normally, but keep close-to-frontal viewing angles<br> - Rotate slowly (to change lighting and background)</p>
in-Vehicle Face Presentation Attack Detection (VFPAD)
<p><strong>Description</strong></p> <p>The in-Vehicle Face Presentation Attack Detection (VFPAD) dataset consists of 4046 bona-fide recordings from 40 subjects, and 1790 attack presentation videos from a total of 89 PAIs (presentation attack instruments). These presentations have been captured using an NIR camera (940 nm) placed on the steering wheel of the car, while NIR illuminators have been fixed on both front pillars (adjacent to the wind-shield) of the car. The bona-fide videos represent 24 male and 16 female subjects of various ethnicities. The PAI species used to construct this dataset include photo-prints, digital displays (for replay attacks), rigid 3D masks, and flexible 3D masks made of silicone.</p> <p> </p> <p><strong>Data Collection</strong></p> <p>The videos comprising this dataset represent bona-fide and attack presentations under a range of variations:</p> <ul> <li>Environmental variations: presentations have been recorded in four sessions, each under different environmental conditions (outdoor sunny; outdoor cloudy; indoor dimly-lit; and indoor brightly-lit)</li> <li>Different scenarios: bona-fide presentations for each subject have been captured with variety of appearances: with/without glasses, with/without hat, etc.</li> <li>Illumination variations: two illumination conditions have been used: ‘uniform’ (both NIR illuminators switched on), and ‘non-uniform’ (only the left NIR-illuminator switched on), and</li> <li>Pose variations: two poses (‘angles’) have been used: ‘front’: the subject looks ahead at the road; and ‘below’: subject looks straight into the camera.</li> </ul> <p> </p> <p><strong>Citation</strong></p> <p>If you use the dataset, please cite the following publication:</p> <p>@article{IEEE_TBIOM_2021,<br> author = {Kotwal, Ketan and Bhattacharjee, Sushil and Abbet, Philip and Mostaani, Zohreh and Wei, Huang and Wenkang, Xu and Yaxi, Zhao and Marcel, S\'{e}bastien},<br> title = {Domain-Specific Adaptation of CNN for Detecting Face Presentation Attacks in NIR},<br> journal = {IEEE Transactions on Biometrics, Behavior, and Identity Science},<br> publisher = {{IEEE}},<br> year={2022},<br> volume={4},<br> number={1},<br> pages={135--147},<br> doi={10.1109/TBIOM.2022.3143569}<br> }</p>
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.