IFTT-PIN: Demonstrating the Self-Calibration Paradigm on a PIN-Entry
Task
- URL: http://arxiv.org/abs/2204.02341v1
- Date: Tue, 5 Apr 2022 16:56:40 GMT
- Title: IFTT-PIN: Demonstrating the Self-Calibration Paradigm on a PIN-Entry
Task
- Authors: Jonathan Grizou
- Abstract summary: IFTT-PIN is a self-calibrating version of the PIN-entry method introduced in Roth et al. 2004.
It infers both the user's PIN and their preferred button-to-color mapping at the same time, a process called self-calibration.
- Score: 4.111899441919164
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: We demonstrate IFTT-PIN, a self-calibrating version of the PIN-entry method
introduced in Roth et al. (2004) [1]. In [1], digits are split into two sets
and assigned a color respectively. To communicate their digit, users press the
button with the same color that is assigned to their digit, which can be
identified by elimination after a few iterations. IFTT-PIN uses the same
principle but does not pre-assign colors to each button. Instead, users are
free to choose which button to use for each color. IFTT-PIN infers both the
user's PIN and their preferred button-to-color mapping at the same time, a
process called self-calibration. Different versions of IFTT-PIN can be tested
at https://jgrizou.github.io/IFTT-PIN/ and a video introduction at
https://youtu.be/5I1ibPJdLHM.
Related papers
- IFTT-PIN: A Self-Calibrating PIN-Entry Method [15.87768582998229]
We demonstrate a novel method that enables the personalising of an interface without the need for explicit calibration procedures.
A second-order effect of self-calibration is that an outside observer cannot easily infer what a user is trying to achieve.
We develop IFTT-PIN as the first self-calibrating PIN-entry method.
arXiv Detail & Related papers (2024-07-02T13:58:28Z) - BERT-PIN: A BERT-based Framework for Recovering Missing Data Segments in
Time-series Load Profiles [10.57410710111382]
BERT-PIN is a Bidirectional Representations from Transformers powered Profile Inpainting Network.
It recovers multiple missing data segments (MDSs) using load and temperature time-series profiles as inputs.
We develop and evaluate BERT-PIN using real-world dataset for two applications: MDSs recovery and demand response baseline estimation.
arXiv Detail & Related papers (2023-10-26T19:30:31Z) - AFR-Net: Attention-Driven Fingerprint Recognition Network [47.87570819350573]
We improve initial studies on the use of vision transformers (ViT) for biometric recognition, including fingerprint recognition.
We propose a realignment strategy using local embeddings extracted from intermediate feature maps within the networks to refine the global embeddings in low certainty situations.
This strategy can be applied as a wrapper to any existing deep learning network (including attention-based, CNN-based, or both) to boost its performance.
arXiv Detail & Related papers (2022-11-25T05:10:39Z) - MultiBiSage: A Web-Scale Recommendation System Using Multiple Bipartite
Graphs at Pinterest [53.3951260443916]
Graph Convolutional Networks (GCN) can efficiently integrate graph structure and node features to learn high-quality node embeddings.
At Pinterest, we have developed and deployed PinSage, a data-efficient GCN that learns pin embeddings from the Pin-Board graph.
We show that training deep learning models on graphs that captures diverse interactions would result in learning higher-quality pin embeddings.
arXiv Detail & Related papers (2022-05-21T20:04:46Z) - IFTT-PIN: A PIN-Entry Method Leveraging the Self-Calibration Paradigm [4.111899441919164]
IFTT-PIN is a self-calibrating version of the PIN-entry method introduced in Roth et al. 2004.
It infers both the user's PIN and their preferred button-to-color mapping at the same time, a process called self-calibration.
We present online interactive demonstrations of IFTT-PIN, with and without self-calibration.
arXiv Detail & Related papers (2022-05-19T12:57:55Z) - BioTouchPass: Handwritten Passwords for Touchscreen Biometrics [3.867363075280544]
This work enhances traditional authentication systems based on Personal Identification Numbers (PIN) and One-Time Passwords (OTP)
In our proposed approach, users draw each digit of the password on the touchscreen of the device instead of typing them as usual.
A complete analysis of our proposed biometric system is carried out regarding the discriminative power of each handwritten digit and the robustness when increasing the length of the password and the number of enrolment samples.
arXiv Detail & Related papers (2022-05-03T07:42:47Z) - Inverse is Better! Fast and Accurate Prompt for Few-shot Slot Tagging [54.557406779183495]
We introduce an inverse paradigm for prompting. Different from the classic prompts mapping tokens to labels, we reversely predict slot values given slot types.
We find, somewhat surprisingly, the proposed method not only predicts faster but also significantly improves the effect (improve over 6.1 F1-scores on 10-shot setting)
arXiv Detail & Related papers (2022-04-02T15:41:19Z) - Mobile Behavioral Biometrics for Passive Authentication [65.94403066225384]
This work carries out a comparative analysis of unimodal and multimodal behavioral biometric traits.
Experiments are performed over HuMIdb, one of the largest and most comprehensive freely available mobile user interaction databases.
In our experiments, the most discriminative background sensor is the magnetometer, whereas among touch tasks the best results are achieved with keystroke.
arXiv Detail & Related papers (2022-03-14T17:05:59Z) - Hand Me Your PIN! Inferring ATM PINs of Users Typing with a Covered Hand [33.26006726271844]
The European Central Bank reported more than 11 billion cash withdrawals and loading/unloading transactions on the European ATMs in 2019.
The PIN mechanism is vulnerable to shoulder-surfing attacks performed via hidden cameras installed near the ATM.
This paper proposes a novel attack to reconstruct PINs entered by victims covering the typing hand with the other hand.
arXiv Detail & Related papers (2021-10-15T14:25:41Z) - Beta-CROWN: Efficient Bound Propagation with Per-neuron Split
Constraints for Complete and Incomplete Neural Network Verification [151.62491805851107]
We develop $beta$-CROWN, a bound propagation based verifier that can fully encode per-neuron splits.
$beta$-CROWN is close to three orders of magnitude faster than LP-based BaB methods for robustness verification.
By terminating BaB early, our method can also be used for incomplete verification.
arXiv Detail & Related papers (2021-03-11T11:56:54Z) - Federated Learning of User Authentication Models [69.93965074814292]
We propose Federated User Authentication (FedUA), a framework for privacy-preserving training of machine learning models.
FedUA adopts federated learning framework to enable a group of users to jointly train a model without sharing the raw inputs.
We show our method is privacy-preserving, scalable with number of users, and allows new users to be added to training without changing the output layer.
arXiv Detail & Related papers (2020-07-09T08:04:38Z)
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.