Keystroke Biometrics Verification System with Dual-branch Architecture, Attention Mechanisms, and Set2set Loss

Grunnleggende konsepter

A novel deep learning-based keystroke biometrics verification system, called Type2Branch, that achieves state-of-the-art performance on large-scale datasets through the use of a dual-branch architecture, attention mechanisms, synthetic data generation, and a novel Set2set loss function.

Sammendrag

The proposed Type2Branch model for keystroke biometrics verification consists of the following key components:

Dual-branch architecture: The model has a recurrent branch and a convolutional branch to capture both the conscious decision process and the unconscious motor process involved in typing behavior.

Attention mechanisms: The model uses self-attention in the recurrent branch and channel attention in the convolutional branch to better capture the relevant features.

Synthetic data generation: The model incorporates synthetic timing features generated based on the general population profile to help the model learn more subject-specific features.

Set2set loss function: The model uses a novel Set2set loss function that extends the SetMargin loss by considering K sets at a time, allowing the model to map the latent hyperspace more effectively.

Training curriculum: The model is trained with a curriculum of increasing difficulty, starting with the nearest users and gradually including more random users to maintain the global structure of the embedding space.

The proposed Type2Branch model achieves state-of-the-art performance on the Keystroke Verification Challenge - onGoing (KVC-onGoing) dataset, outperforming previous approaches by a significant margin. Considering the mean per-subject distribution, the model achieves an EER of 0.77% and 1.03% on the desktop and mobile evaluation sets, respectively. With a uniform global threshold, the EERs are 3.25% for desktop and 3.61% for mobile, demonstrating the model's effectiveness in large-scale keystroke biometrics verification.

Statistikk

The proposed model was evaluated on the Aalto desktop and mobile keystroke dynamics datasets, which contain over 185,000 subjects in total.

Sitater

"Type2Branch improves previous verification records in all cases, by a significant margin."
"Considering global distributions, the EER obtained by Type2Branch (3.33%) is halved in comparison with TypeNet (6.75%) in the desktop case, and it is reduced to almost one third of the EER scored by TypeFormer in the mobile case (3.61% vs 9.45%)."
"Type2Branch significantly outperforms previous approaches, showing that the Set2set loss proposed is able to map the embedding space in a much finer way with respect to the triplet loss or the original SetMargin loss, leading to unprecedented results in the identification task as well."

Viktige innsikter hentet fra

Type2Branch: Keystroke Biometrics based on a Dual-branch Architecture with Attention Mechanisms and Set2set Loss

by Nahu... klokken arxiv.org 05-03-2024

https://arxiv.org/pdf/2405.01088.pdf

Dypere Spørsmål

How would the performance of Type2Branch be affected by incorporating free-text typing samples in addition to the transcript-text samples used in the current evaluation

Incorporating free-text typing samples in addition to the transcript-text samples used in the current evaluation would likely have both positive and negative effects on the performance of Type2Branch.
Positive Effects:

Increased Variability: Free-text samples would introduce more variability in the typing patterns captured by the model. This increased variability could potentially improve the model's ability to distinguish between users, leading to better verification performance.
Real-World Scenario Simulation: Free-text samples better simulate real-world typing scenarios where users are not constrained to specific sentences or phrases. This could enhance the model's robustness and generalization capabilities.

Negative Effects:

Data Sparsity: Free-text samples are likely to be more sparse and less structured compared to transcript-text samples. This could make it challenging for the model to extract meaningful features and patterns, potentially leading to decreased performance.
Increased Noise: Free-text samples may contain more errors, inconsistencies, and variations in typing behavior, which could introduce noise into the model and impact its accuracy.

Incorporating free-text typing samples would require careful data preprocessing, feature engineering, and model adaptation to effectively leverage the additional information while mitigating potential drawbacks.

What advanced attack models, beyond the zero-effort impostor assumption, could be used to further evaluate the robustness of the Type2Branch model

To further evaluate the robustness of the Type2Branch model, advanced attack models beyond the zero-effort impostor assumption could be employed. These advanced attack models aim to simulate more sophisticated and realistic impostor scenarios, challenging the model's ability to differentiate between genuine users and impostors. Some advanced attack models that could be considered include:

Synthetic Forgeries: Generating synthetic keystroke samples that mimic the typing behavior of legitimate users to test the model's resilience against sophisticated impostor attacks.

Adversarial Attacks: Introducing adversarial perturbations to keystroke samples to deceive the model and evaluate its vulnerability to targeted attacks.

Behavioral Mimicry: Developing impostor samples that closely mimic the typing behavior of specific genuine users to assess the model's ability to detect subtle differences and anomalies.

Transfer Learning Attacks: Leveraging transfer learning techniques to transfer knowledge from one user to another, challenging the model's ability to distinguish between genuine and transferred behaviors.

By subjecting the Type2Branch model to these advanced attack models, researchers can gain deeper insights into its strengths and weaknesses, leading to more comprehensive evaluations and potential improvements in its security and performance.

Given the promising results in keystroke biometrics, how could the techniques used in Type2Branch be applied to other behavioral biometric modalities, such as gait or touch gestures, to improve their verification performance

The techniques used in Type2Branch for keystroke biometrics could be applied to other behavioral biometric modalities, such as gait or touch gestures, to enhance their verification performance. Here's how these techniques could be adapted:

Feature Extraction: Similar to keystroke dynamics, gait and touch gestures exhibit unique patterns that can be captured through feature extraction techniques. By synthesizing timing features and emphasizing user behavior deviations, the model can learn subject-specific characteristics for gait and touch gestures.

Dual-Branch Architecture: Implementing a dual-branch architecture, combining recurrent and convolutional paths with attention mechanisms, can help extract both temporal and spatial features from gait and touch gesture data. This approach can enhance the model's ability to capture the distinct biometric traits of individuals.

Loss Function Optimization: Utilizing specialized loss functions like Set2set loss can help map the embedding space more effectively for gait and touch gesture biometrics. By considering the global structure of the feature space, the model can improve verification accuracy and robustness.

Training Curriculum: Designing a learning curriculum of increasing difficulty, similar to Type2Branch, can help the model adapt to a wide range of user behaviors in gait and touch gesture modalities. This progressive training approach can enhance the model's performance under varying conditions and scenarios.

By applying these techniques to gait and touch gesture biometrics, researchers can potentially achieve state-of-the-art verification performance, similar to the advancements seen in keystroke dynamics with the Type2Branch model.