IEEE BigData 2023 Keystroke Verification Challenge (KVC)

• URL: http://arxiv.org/abs/2401.16559v1
• Date: Mon, 29 Jan 2024 20:51:42 GMT
• Title: IEEE BigData 2023 Keystroke Verification Challenge (KVC)
• Authors: Giuseppe Stragapede and Ruben Vera-Rodriguez and Ruben Tolosana and Aythami Morales and Ivan DeAndres-Tame and Naser Damer and Julian Fierrez and Javier-Ortega Garcia and Nahuel Gonzalez and Andrei Shadrikov and Dmitrii Gordin and Leon Schmitt and Daniel Wimmer and Christoph Grossmann and Joerdis Krieger and Florian Heinz and Ron Krestel and Christoffer Mayer and Simon Haberl and Helena Gschrey and Yosuke Yamagishi and Sanjay Saha and Sanka Rasnayaka and Sandareka Wickramanayake and Terence Sim and Weronika Gutfeter and Adam Baran and Mateusz Krzyszton and Przemyslaw Jaskola
• Abstract summary: This paper considers the biometric verification performance of Keystroke Dynamics captured as tweet-long sequences of variable transcript text from over 185,000 subjects. The data are obtained from two of the largest public databases of KD up to date. Several neural architectures were proposed by the participants, leading to global Equal Error Rates (EERs) as low as 3.33% and 3.61% achieved by the best team respectively.
• Score: 14.366081634293721
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: This paper describes the results of the IEEE BigData 2023 Keystroke Verification Challenge (KVC), that considers the biometric verification performance of Keystroke Dynamics (KD), captured as tweet-long sequences of variable transcript text from over 185,000 subjects. The data are obtained from two of the largest public databases of KD up to date, the Aalto Desktop and Mobile Keystroke Databases, guaranteeing a minimum amount of data per subject, age and gender annotations, absence of corrupted data, and avoiding excessively unbalanced subject distributions with respect to the considered demographic attributes. Several neural architectures were proposed by the participants, leading to global Equal Error Rates (EERs) as low as 3.33% and 3.61% achieved by the best team respectively in the desktop and mobile scenario, outperforming the current state of the art biometric verification performance for KD. Hosted on CodaLab, the KVC will be made ongoing to represent a useful tool for the research community to compare different approaches under the same experimental conditions and to deepen the knowledge of the field.

Related papers

• Decorrelating Structure via Adapters Makes Ensemble Learning Practical for Semi-supervised Learning [50.868594148443215]
  In computer vision, traditional ensemble learning methods exhibit either a low training efficiency or the limited performance. We propose a lightweight, loss-function-free, and architecture-agnostic ensemble learning by the Decorrelating Structure via Adapters (DSA) for various visual tasks.
  arXiv  Detail & Related papers  (2024-08-08T01:31:38Z)
• IAI Group at CheckThat! 2024: Transformer Models and Data Augmentation for Checkworthy Claim Detection [1.3686993145787067]
  This paper describes IAI group's participation for automated check-worthiness estimation for claims. The task involves the automated detection of check-worthy claims in English, Dutch, and Arabic political debates and Twitter data. We utilize various pre-trained generative decoder and encoder transformer models, employing methods such as few-shot chain-of-thought reasoning.
  arXiv  Detail & Related papers  (2024-08-02T08:59:09Z)
• Type2Branch: Keystroke Biometrics based on a Dual-branch Architecture with Attention Mechanisms and Set2set Loss [5.23087771966975]
  This article describes Type2Branch, the model and techniques that achieved the lowest error rates at the KVC-onGoing competition. Considering five enrollment samples per subject of approximately 50 characters typed, the proposed Type2Branch achieves state-of-the-art performance. With a uniform global threshold for all subjects, the EERs are 3.25% for desktop and 3.61% for mobile, outperforming previous approaches by a significant margin.
  arXiv  Detail & Related papers  (2024-05-02T08:33:43Z)
• AIGIQA-20K: A Large Database for AI-Generated Image Quality Assessment [54.93996119324928]
  We create the largest AIGI subjective quality database to date with 20,000 AIGIs and 420,000 subjective ratings, known as AIGIQA-20K. We conduct benchmark experiments on this database to assess the correspondence between 16 mainstream AIGI quality models and human perception.
  arXiv  Detail & Related papers  (2024-04-04T12:12:24Z)
• Keystroke Verification Challenge (KVC): Biometric and Fairness Benchmark Evaluation [21.63351064421652]
  Keystroke dynamics (KD) for biometric verification has several advantages. KD is among the most discriminative behavioral traits. We present a new experimental framework to benchmark KD-based biometric verification performance and fairness.
  arXiv  Detail & Related papers  (2023-11-10T11:23:28Z)
• Wild Face Anti-Spoofing Challenge 2023: Benchmark and Results [73.98594459933008]
  Face anti-spoofing (FAS) is an essential mechanism for safeguarding the integrity of automated face recognition systems. This limitation can be attributed to the scarcity and lack of diversity in publicly available FAS datasets. We introduce the Wild Face Anti-Spoofing dataset, a large-scale, diverse FAS dataset collected in unconstrained settings.
  arXiv  Detail & Related papers  (2023-04-12T10:29:42Z)
• BeCAPTCHA-Type: Biometric Keystroke Data Generation for Improved Bot Detection [63.447493500066045]
  This work proposes a data driven learning model for the synthesis of keystroke biometric data. The proposed method is compared with two statistical approaches based on Universal and User-dependent models. Our experimental framework considers a dataset with 136 million keystroke events from 168 thousand subjects.
  arXiv  Detail & Related papers  (2022-07-27T09:26:15Z)
• My(o) Armband Leaks Passwords: An EMG and IMU Based Keylogging Side-Channel Attack [8.195365677760211]
  We take a look at the potential of using electromyographic (EMG) data, a sensor modality which is new to the market. In our approach, the EMG data has proven to be the most prominent source of information compared to the accelerometer and gyroscope. We have created an extensive dataset including more than 310 000 keystrokes recorded from 37 volunteers.
  arXiv  Detail & Related papers  (2021-12-04T16:48:56Z)
• Semi-Supervised Domain Generalizable Person Re-Identification [74.75528879336576]
  Existing person re-identification (re-id) methods are stuck when deployed to a new unseen scenario. Recent efforts have been devoted to domain adaptive person re-id where extensive unlabeled data in the new scenario are utilized in a transductive learning manner. We aim to explore multiple labeled datasets to learn generalized domain-invariant representations for person re-id.
  arXiv  Detail & Related papers  (2021-08-11T06:08:25Z)
• GAIA: A Transfer Learning System of Object Detection that Fits Your Needs [136.60609274344893]
  Transfer learning with pre-training on large-scale datasets has played an increasingly significant role in computer vision and natural language processing. In this paper, we focus on the area of object detection and present a transfer learning system named GAIA. GAIA is capable of providing powerful pre-trained weights, selecting models that conform to downstream demands such as latency constraints and specified data domains.
  arXiv  Detail & Related papers  (2021-06-21T18:24:20Z)
• A Robust Comparison of the KDDCup99 and NSL-KDD IoT Network Intrusion Detection Datasets Through Various Machine Learning Algorithms [0.0]
  Two of the most cited intrusion detection datasets are the KDDCup99 and the NSL-KDD. The main goal of our project was to conduct a robust comparison of both datasets. We were able to conclude that the NSL-KDD dataset is of a higher quality than the KDDCup99 dataset.
  arXiv  Detail & Related papers  (2019-12-31T07:36:33Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.