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11 results for “Spoofing”
PartialSpoof Database - Partially Spoofed Audio Dataset for Anti-spoofing
<p>All existing databases of spoofed speech contain attack data that is spoofed in its entirety. In practice, it is entirely plausible that successful attacks can be mounted with utterances that are only partially spoofed. By definition, partially-spoofed utterances contain a mix of both spoofed and bona fide segments, which will likely degrade the performance of countermeasures trained with entirely spoofed utterances. This hypothesis raises the obvious question: ‘Can we detect partially spoofed audio?’ This paper introduces a new database of partially-spoofed data, named <strong>PartialSpoof</strong>, to help address this question. This new database enables us to investigate and compare the performance of countermeasures on both utterance- and segmental- level labels. Experimental results using the utterance-level labels reveal that the reliability of countermeasures trained to detect fully-spoofed data is found to degrade substantially when tested with partially-spoofed data, whereas training on partially-spoofed data performs reliably in the case of both fully- and partially- spoofed utterances. Additional experiments using segmental-level labels show that spotting injected spoofed segments included in an utterance is a much more challenging task even if the latest countermeasure models are used.</p> <p> </p> <ul> <li><strong>!!!NEW!!! For detailed (bonafide/spoofing methods/nonspeech/concatenated parts) timestamps of PartialSpoof v1.3</strong> <ul> <li><a href="https://drive.google.com/drive/folders/1kKW3GBuooPkAl64Zyv6WPgICH5LZtnQR">Google Drive</a> </li> <li>The official version is under preparation. Please download this one if you urgently need it.</li> </ul> </li> <li>For fine-grained labels of PartialSpoof v1.2 <ul> <li>Arxiv: http://arxiv.org/abs/2204.05177</li> <li>PartialSpoof Database v1.2<strong> </strong>(including segmental-level labels in different temporal resolutions and timestamp labels)<strong>: This one</strong></li> </ul> </li> <li>For the multi-task version of PartialSpoof <strong>v1.1</strong> <ul> <li>Arxiv: https://arxiv.org/abs/2107.14132</li> <li>PartialSpoof Database v1.1 (including 0.16s segmental level labels): https://zenodo.org/record/5112031</li> </ul> </li> <li>For the initial version of PartialSpoof <strong>v1.0</strong> <ul> <li>Arxiv: https://arxiv.org/abs/2104.02518</li> <li>Samples: https://nii-yamagishilab.github.io/zlin-demo/IS2021/index.html</li> <li>PartialSpoof Database v1.0: https://zenodo.org/record/4817532</li> </ul> </li> </ul> <p>P.S.</p> <p>1. Compared to the <a href="../record/4817532#.YLO07S2l1hE">PartialSpoof_v1.0</a> and <a href="../record/5112031">PartialSpoof_v1.1</a>, only <strong>database_segment_labels_v1.2.tar.gz, database_vad.tar.gz, </strong> and<strong> README_v1.2</strong> are updated for version 1.2, you don't need to download other files if you already downloaded version1.0 or 1.1.</p> <p>2. File database_eval.tar.gz is a little large, if you cannot download it smoothly, you can download the split database_eval.tar.gz from <a href="../record/4817532#.YLO07S2l1hE">PartialSpoof_v1.0</a> </p>
ASVspoof2019LA-Sim: Augmented Dataset for An Empirical Study on Channel Effects for Synthetic Voice Spoofing Countermeasure Systems
<p>This is the dataset we augmented to study the channel effects for anti-spoofing. For more details, please refer to our Interspeech 2021 paper: "An Empirical Study on Channel Effects for Synthetic Voice Spoofing Countermeasure Systems".</p> <p>Proceeding: <a href="https://www.isca-speech.org/archive/interspeech_2021/zhang21ea_interspeech.html">https://www.isca-speech.org/archive/interspeech_2021/zhang21ea_interspeech.html</a></p> <p>Arxiv: <a href="https://arxiv.org/pdf/2104.01320.pdf">https://arxiv.org/pdf/2104.01320.pdf</a></p> <p>Code: <a href="https://github.com/yzyouzhang/Empirical-Channel-CM">https://github.com/yzyouzhang/Empirical-Channel-CM</a></p> <p>Contact: you.zhang@rochester.edu</p> <p><strong>Version 1.0</strong> contains the <strong>training</strong> and the <strong>development</strong> set. We have added the <strong>evaluation</strong> set in <strong>version 1.1 </strong>but deleted the training set due to the size limitation, but you can still access the training set in version 1.0.</p> <p>Please check it out.</p> <p>To extract the files, please use the following commands:</p> <pre><code class="language-bash">cat eval.tar.gz-part* > eval.tar.gz tar -xvzf *.tar.gz</code></pre> <p>After concatenation, to make sure the download is complete, you can check with the following:</p> <pre><code>md5sum *.tar.gz 15dea7d28b126994bb6b159778f706af dev.tar.gz 0615052b34ca6c7f58505eaa8647844f eval.tar.gz 3058dd9d407f3c9ae697acca8c34a6c3 train.tar.gz</code></pre> <p>Thanks.</p>
GATEMAN project, GNSS raw data in presence of spoofing
<p>GNSS raw data generated during the in-lab validation activities of spoofing detection and localization performed in the frame of the <strong>GATEMAN</strong> project. These files are grouped for each type of validation scenario defined. A word file describing the test setup is included.</p>
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>
MARitime SIMulated (MARSIM) Dataset for GPS Spoofing Detection
<p>MARitime SIMulated (MARSIM) is a comprehensive and labeled dataset created to test and evaluate NMEA-based anomaly detection approaches related to GNSS/GPS spoofing attacks in the maritime context, e.g. <a href="https://github.com/fkie-cad/mana">MANA</a>. It consists of multiple scenarios (benign and attacked) with a duration of 120 s, provided as network traffic recordings, i.e., in packet capture (pcap) format. Each recording contains the NMEA 0183 sentences transmitted within the network of a single vessel heading north from a fixed point with constant velocity. The recordings are reduced to two PNT transmitting devices, namely the pair of GPS receivers, which are placed at a fixed distance of 4 m from each other.</p> <p>Working with the provided packet captures can be facilitated by using our <a href="https://github.com/fkie-cad/maritime-dissector">Wireshark dissector</a>.</p> <p>For more information, please see Spravil J, Hemminghaus C, von Rechenberg M, Padilla E, Bauer J. Detecting Maritime GPS Spoofing Attacks Based on NMEA Sentence Integrity Monitoring. Journal of Marine Science and Engineering. 2023; 11(5):928. DOI: <a href="https://doi.org/10.3390/jmse11050928">10.3390/jmse11050928</a></p>
CpAug: Refining Copy-Paste Augmentation for Speech Anti-Spoofing
<p>Conventional copy-paste augmentations generate new training instances by concatenating existing utterances to increase the amount of data for neural network training. However, the direct application of copy-paste augmentation for anti-spoofing is problematic. This paper refines the copy-paste augmentation for speech anti-spoofing, dubbed CpAug, to generate more training data with rich intra-class diversity. The CpAug employs two policies: concatenation to merge utterances with identical labels, and substitution to replace segments in an anchor utterance. Besides, considering the impacts of speakers and spoofing attack types, we craft four blending strategies for the CpAug. Furthermore, we explore how CpAug complements the Rawboost augmentation method. Experimental results reveal that the proposed CpAug significantly improves the performance of speech anti-spoofing. Particularly, CpAug with substitution policy leads to relative improvements of 43% and 38% on the ASVspoof’ 19LA and 21LA, respectively. Notably, the CpAug and Rawboost synergize effectively, achieving an EER of 2.91% on ASVspoof’ 21LA.</p>
Latin-American voice anti-spoofing dataset
<p>This dataset contains samples of spoof and real human voice with different accents from Latin-American countries.</p> <p> Table 1. Real samples distribution</p> <table> <tbody> <tr> <td><strong>Accent</strong></td> <td><strong>Gender</strong></td> <td><strong># Speakers</strong></td> <td><strong># Files</strong></td> <td><strong>Nomenclature</strong></td> </tr> <tr> <td>Colombian</td> <td> <table> <tbody> <tr> <td>Male</td> </tr> <tr> <td>Female</td> </tr> </tbody> </table> </td> <td> <table> <tbody> <tr> <td>17</td> </tr> <tr> <td>14</td> </tr> </tbody> </table> </td> <td> <table> <tbody> <tr> <td>2534</td> </tr> <tr> <td>2070</td> </tr> </tbody> </table> </td> <td> <table> <tbody> <tr> <td>com</td> </tr> <tr> <td>cof</td> </tr> </tbody> </table> </td> </tr> <tr> <td>Chilean</td> <td> <table> <tbody> <tr> <td>Male</td> </tr> <tr> <td>Female</td> </tr> </tbody> </table> </td> <td> <table> <tbody> <tr> <td>17</td> </tr> <tr> <td>12</td> </tr> </tbody> </table> </td> <td> <table> <tbody> <tr> <td>2487</td> </tr> <tr> <td>1602</td> </tr> </tbody> </table> </td> <td> <table> <tbody> <tr> <td>clm</td> </tr> <tr> <td>clf</td> </tr> </tbody> </table> </td> </tr> <tr> <td>Peruvian</td> <td> <table> <tbody> <tr> <td>Male</td> </tr> <tr> <td>Female</td> </tr> </tbody> </table> </td> <td> <table> <tbody> <tr> <td>20</td> </tr> <tr> <td>18</td> </tr> </tbody> </table> </td> <td> <table> <tbody> <tr> <td>2917</td> </tr> <tr> <td>2529</td> </tr> </tbody> </table> </td> <td> <table> <tbody> <tr> <td>pem</td> </tr> <tr> <td>pef</td> </tr> </tbody> </table> </td> </tr> <tr> <td>Venezuelan</td> <td> <table> <tbody> <tr> <td>Male</td> </tr> <tr> <td>Female</td> </tr> </tbody> </table> </td> <td> <table> <tbody> <tr> <td>12</td> </tr> <tr> <td>10</td> </tr> </tbody> </table> </td> <td> <table> <tbody> <tr> <td>1754</td> </tr> <tr> <td>1463</td> </tr> </tbody> </table> </td> <td> <table> <tbody> <tr> <td>vem</td> </tr> <tr> <td>vef</td> </tr> </tbody> </table> </td> </tr> <tr> <td>Argentinian</td> <td> <table> <tbody> <tr> <td>Male</td> </tr> <tr> <td>Female</td> </tr> </tbody> </table> </td> <td> <table> <tbody> <tr> <td>12</td> </tr> <tr> <td>30</td> </tr> </tbody> </table> </td> <td> <table> <tbody> <tr> <td>1670</td> </tr> <tr> <td>3790</td> </tr> </tbody> </table> </td> <td> <table> <tbody> <tr> <td>arm</td> </tr> <tr> <td>arf</td> </tr> </tbody> </table> </td> </tr> <tr> <td>Total</td> <td> </td> <td>162</td> <td>22816</td> <td> </td> </tr> </tbody> </table> <p> </p> <p>The bonafide samples were obtained from the following sources:</p> <ul> <li>Colombian accents: <a href="https://www.openslr.org/72/">https://www.openslr.org/72/</a> (<a href="https://www.openslr.org/resources/75/LICENSE">License</a>)</li> <li>Chilean accents: <a href="https://www.openslr.org/71/">https://www.openslr.org/71/</a> (<a href="https://www.openslr.org/resources/75/LICENSE">License</a>)</li> <li>Peruvian accents: <a href="https://www.openslr.org/73/">https://www.openslr.org/73/</a> (<a href="https://www.openslr.org/resources/75/LICENSE">License</a>)</li> <li>Venezuelan accents: <a href="https://www.openslr.org/75/">https://www.openslr.org/75/</a> (<a href="https://www.openslr.org/resources/75/LICENSE">License</a>)</li> <li>Argentinian accents: <a href="https://www.openslr.org/61/">https://www.openslr.org/61/</a> (<a href="https://www.openslr.org/resources/75/LICENSE">License</a>)</li> </ul> <p> </p> <p>The strategies used to generate the spoof samples:</p> <p> Table 2. Spoof Samples distribution</p> <table> <tbody> <tr> <td><strong>Name</strong></td> <td><strong>Type</strong></td> <td><strong>#Samples</strong></td> </tr> <tr> <td>StarGAN</td> <td>Voice conversion</td> <td>16000</td> </tr> <tr> <td>CycleGAN</td> <td>Voice conversion</td> <td>16000</td> </tr> <tr> <td>Diffusion</td> <td>Voice conversion</td> <td>16000</td> </tr> <tr> <td>TTS</td> <td>Text-to-speech</td> <td>5000</td> </tr> <tr> <td>TTS-StarGAN</td> <td>Text-to-speech / Voice conversion</td> <td>2500</td> </tr> <tr> <td>TTS-Diff</td> <td>Text-to-speech / Voice conversion</td> <td>2500</td> </tr> </tbody> </table> <p> </p> <ul> <li><a href="https://arxiv.org/abs/1806.02169">StarGAN-VC: Non-parallel many-to-many Voice Conversion Using Star Generative Adversarial Networks</a></li> <li><a href="https://ieeexplore.ieee.org/document/8553236">Cyclegan-VC: Non-parallel voice conversion using cycle-consistent adversarial networks</a></li> <li><a href="https://arxiv.org/abs/2109.13821">Diffusion-Based Voice Conversion with Fast Maximum Likelihood Sampling Scheme</a></li> <li>TTS: Microsoft azure TTS</li> <li>TTS-VC: Microsoft azure TTS + StarGAN/Diff</li> </ul> <p> </p> <p> Table 3. Dataset overview</p> <table> <tbody> <tr> <td><strong>Audio Samples</strong></td> <td><strong>Human Speakers</strong></td> <td><strong>Spoofing algorithms</strong></td> <td><strong>Sampling rate</strong></td> </tr> <tr> <td> <table> <tbody> <tr> <td>Bonafide</td> <td>Spoof</td> </tr> <tr> <td>22816</td> <td>58000</td> </tr> </tbody> </table> </td> <td> <table> <tbody> <tr> <td>Male</td> <td>Female</td> </tr> <tr> <td>78</td> <td>84</td> </tr> </tbody> </table> </td> <td> <table> <tbody> <tr> <td>VC</td> <td>TTS</td> <td>VC and TTS</td> </tr> <tr> <td>3</td> <td>1</td> <td>2</td> </tr> </tbody> </table> </td> <td>16kHz</td> </tr> </tbody> </table> <p> </p> <p>On the <em>protocol.txt</em> file is listed all the files with the following structure:</p> <p><em> Subject_id file_name – spoof_type Label</em></p> <p>Consider this line on protocol.txt file:<br> <em>arf_00295 StarGAN-arf _00295_01349969200-cof _03349 _0077577 - StarGAN spoof</em><br> The first part (arf_00295) represent the subject id, from which we can also identify the accent and the gender (see nomenclature column on Table 1). The file name identify the type of spoof following for the source audio file and the target file. StarGAN represents the type of spoof. According to the table 2, this method is a Voice Conversion algorithm. If the file is a bonafide sample, we replace the <em>spoof_type</em> with a dash (-). Finally at the end of the line we refer the kind of label of the file, in the example, the file corresponds to a spoof case.</p> <p>Each zip file contains 6 folders, each one holds a type of samples. For the voice conversion folders, there are 25 sub-folders that indicate the conversion between accents. For example, Argentina-Venezuela folder indicates that the source accent of the file is Argentinian and the target is Venezuelan accent. Inside the folder there are 64 sub-folders that represent the subjects used for the conversion. For instance, the folder arf_00295-vem_04310 means that the source is an Argentinean female and the target is a Venezuelan male (see Table 1 for nomenclature). In the case of a Text-to-Speech folder there are 5 sub-folders that represent the accents. A TTS-VC folder there are 2 sub-folders that represent the voice conversion strategy used. Inside there are other sub-folders for the different combinations of source and target accents.</p> <p>You can check the folder tree structure in the tree.txt file. Table 3 shows a summary of the resulting dataset.</p> <p> </p>
Spoofing Signal Generation Algorithm using the Processing Information of a Receiver for Authentic GPS Signals
<p>Experimental results analyzation files.</p>
Anti Spoofing Real Dataset
Open the record for dataset details and reuse information.
ASVspoof 2019: The 3rd Automatic Speaker Verification Spoofing and Countermeasures Challenge database
<p>This is a database used for the Third Automatic Speaker Verification Spoofing and Countermeasures Challenge, for short, ASVspoof 2019 (http://www.asvspoof.org) organized by Junichi Yamagishi, Massimiliano Todisco, Md Sahidullah, Héctor Delgado, Xin Wang, Nicholas Evans, Tomi Kinnunen, Kong Aik Lee, Ville Vestman, and Andreas Nautsch in 2019. The ASVspoof challenge aims to encourage further progress through (i) the collection and distribution of a standard dataset with varying spoofing attacks implemented with multiple, diverse algorithms and (ii) a series of competitive evaluations for automatic speaker verification. The ASVspoof 2019 challenge follows on from three special sessions on spoofing and countermeasures for automatic speaker verification held during INTERSPEECH 2013, 2015, and 2017. While the first edition in 2013 was targeted mainly at increasing awareness of the spoofing problem, the 2015 edition included the first challenge on the topic, accompanied by commonly defined evaluation data, metrics and protocols. The task in ASVspoof 2015 was to design countermeasure solutions capable of discriminating between bona fide (genuine) speech and spoofed speech produced using either text-to-speech (TTS) or voice conversion (VC) systems. The ASVspoof 2017 challenge focused on the design of countermeasures aimed at detecting replay spoofing attacks that could, in principle, be implemented by anyone using common consumer-grade devices. The ASVspoof 2019 challenge extends the previous challenge in several directions. The 2019 edition is the first to focus on countermeasures for all three major attack types, namely those stemming from TTS, VC and replay spoofing attacks. Advances with regards to the 2015 edition include the addition of up-to-date TTS and VC systems that draw upon substantial progress made in both fields during the last four years. ASVspoof 2019 thus aims to determine whether the advances in TTS and VC technology post a greater threat to automatic speaker verification and the reliability of spoofing countermeasures. Advances with regards to the 2017 edition concern the use of a far more controlled evaluation setup for the assessment of replay spoofing countermeasures. Whereas the 2017 challenge was created from the recordings of real replayed spoofing attacks, the use of an uncontrolled setup made results somewhat difficult to analyse. A controlled setup, in the form of replay attacks simulated using a range of real replay devices and carefully controlled acoustic conditions is adopted in ASVspoof 2019 with the aim of bringing new insights into the replay spoofing problem. Last but not least, the 2019 edition aligns ASVspoof more closely with the field of automatic speaker verification. Whereas the 2015 and 2017 editions focused on the development and assessment of stand-alone countermeasures, ASVspoof 2019 adopts for the first time a new ASV-centric metric in the form of the tandem decision cost function (t-DCF). The ASVspoof 2019 database encompasses two partitions for the assessment of logical access (LA) and physical access (PA) scenarios. Both are derived from the VCTK base corpus which includes speech data captured from 107 speakers (46 males, 61 females). Both LA and PA databases are themselves partitioned into three datasets, namely training, development and evaluation which comprise the speech from 20 (8 male, 12 female), 10 (4 male, 6 female) and 48 (21 male, 27 female) speakers respectively. The three partitions are disjoint in terms of speakers, and the recording conditions for all source data are identical. While the training and development sets contain spoofing attacks generated with the same algorithms/conditions (designated as known attacks), the evaluation set also contains attacks generated with different algorithms/conditions (designated as unknown attacks). Reliable spoofing detection performance therefore calls for systems that generalise well to previously-unseen spoofing attacks. Full descriptions are available in the ASVspoof 2019 evaluation plan. ## Codec ## The original waveform format is PCM and compressed using Flac. No telephone or mobile codec was used.</p>
Multispectral-Spoof (MSSpoof)
<p><strong>Multispectral-Spoof contains face images and printed spoofing attacks recorded in Visible (VIS) and Near-Infrared (NIR) spectra for 21 identities. </strong></p> <p>Multispectral-Spoof is a dataset for multi-spectral face recognition and presentation attack detection (anti-spoofing). The dataset contains images of Bona Fide VIS and NIR images as well as VIS and NIR printed presentation attacks (spoofing attacks) to VIS and NIR systems. The number of clients in the dataset is 21. The recordings are done using a uEye camera with resolution 1280x1024. When recording the images in NIR, a NIR filter of 800nm has been mounted to the camera.</p> <p> </p> <p><strong>Recording the real accesses </strong></p> <p>The set of real accesses contains recordings of VIS and NIR images for the identities. For each identity, a total of 5 images in VIS and 5 images in NIR are recorded for each of 7 environment conditions. 1 of these conditions is set up in an hallway space, while the rest 6 conditions are set up in an office. So, in total, the database contains 7*5=35 images per client in VIS and 7*5=35 images per client in NIR spectrum. Thus, the total number of real accesses per client is 70.</p> <p> </p> <p><strong>Recording the spoofing attacks </strong></p> <p>For each client in the database, 3 images in VIS and 3 images in NIR are selected from the original database. The chosen images are from the ones with the best quality in terms of recording conditions. These images are then printed on a paper using black & white printed with resolution of 600dpi. During the recording of the spoofing attacks, the printed images are attached on a fixed support. For each of the printed images, we recorded 4 spoofing attacks in 3 lighting conditions, both in VIS and NIR spectra. Thus, the total number of spoofing attacks per client is 6 * 2 * 3 * 4 = 144.</p> <p> </p> <p><strong>Database protocol </strong></p> <p>The recorded images are divided into train, development and test set, and the clients in each of the sets do not overlap. There are 9 clients in the train subset, 6 in the development and 6 in the test subset. The subsets do not overlap, meaning that a client in one subset can not appear in any other subset.</p> <ul> <li>Client IDs in the world subset: 2,4,7,9,10,12,16,17,21</li> <li>Client IDs in the dev subset: 1,3,5,6,8,11</li> <li>Client IDs in the test subset: 13,15,18,19,20,22</li> </ul> <p>Out of the 75 real access images per client, 10 are taken into the enrollment set: 5 from VIS and 5 from NIR spectra.</p> <p> </p> <p><strong>Reference paper</strong></p> <p>I. Chingovska, N. Erdogmus, A. Anjos. S. Marcel, “<strong>Face Recognition Systems Under Spoofing Attacks</strong>”, in Springer “Face Recognition Across the Imaging Spectrum” (Editor Thirimachos Bourlai), 2016.<br> <a href="http://dx.doi.org/10.1007/978-3-319-28501-6_8">10.1007/978-3-319-28501-6_8</a><br> <a href="https://publications.idiap.ch/index.php/publications/show/3539">https://publications.idiap.ch/index.php/publications/show/3539</a></p>
ScienceDex guides
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
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.