Flood prediction is critical for emergency planning and response to mitigate human and economic losses. Traditional physics-based hydrodynamic models generate high-resolution flood maps using numerical methods requiring fine-grid discretization; which are computationally intensive and impractical for real-time large-scale applications. While recent studies have applied convolutional neural networks for flood map super-resolution with good accuracy and speed, they suffer from limited generalizability to unseen areas. In this paper, we propose a novel approach that leverages latent diffusion models to perform super-resolution on coarse-grid flood maps, with the objective of achieving the accuracy of fine-grid flood maps while significantly reducing inference time. Experimental results demonstrate that latent diffusion models substantially decrease the computational time required to produce high-fidelity flood maps without compromising on accuracy, enabling their use in real-time flood risk management. Moreover, diffusion models exhibit superior generalizability across different physical locations, with transfer learning further accelerating adaptation to new geographic regions. Our approach also incorporates physics-informed inputs, addressing the common limitation of black-box behavior in machine learning, thereby enhancing interpretability. Code is available at https://github.com/neosunhan/flood-diff.

Animating stylized head avatars with dynamic poses and expressions has become an important focus in recent research due to its broad range of applications (e.g. VR/AR, film and animation, privacy protection). Previous research has made significant progress by training controllable generative models to animate the reference avatar using the target pose and expression. However, existing portrait animation methods are mostly trained using human faces, making them struggle to generalize to stylized avatar references such as cartoon and painting. Moreover, the mechanisms used to animate avatars—namely, to control the pose and expression of the reference—often inadvertently introduce unintended features—such as facial shape—from the target, while also causing a loss of intended features, like expression-related details. This paper proposes AniFaceDiff, a Stable Diffusion (Rombach et al., 2022)-based method with a new conditioning module for animating stylized avatars. First, we propose a refined spatial conditioning approach by Facial Alignment to minimize identity mismatches, particularly between stylized avatars and human faces. Then, we introduce an Expression Adapter that incorporates additional cross-attention layers to address the potential loss of expression-related information. Extensive experiments demonstrate that our method achieves state-of-the-art performance, particularly in the most challenging out-of-domain stylized avatar animation, i.e., domains unseen during training. It delivers superior image quality, identity preservation, and expression accuracy. This work enhances the quality of virtual stylized avatar animation for constructive and responsible applications. To promote ethical use in virtual environments, we contribute to the advancement of detection for generative content by evaluating state-of-the-art detectors, highlighting potential areas for improvement, and suggesting solutions.

This article presents the Keystroke Verification Challenge - onGoing (KVC-onGoing) 1 1 https://sites.google.com/view/bida-kvc/ . , on which researchers can easily benchmark their systems in a common platform using large-scale public databases, the Aalto University Keystroke databases, and a standard experimental protocol. The keystroke data consist of tweet-long sequences of variable transcript text from over 185,000 subjects, acquired through desktop and mobile keyboards simulating real-life conditions. The results on the evaluation set of KVC-onGoing have proved the high discriminative power of keystroke dynamics, reaching values as low as 3.33% of Equal Error Rate (EER) and 11.96% of False Non-Match Rate (FNMR) @1% False Match Rate (FMR) in the desktop scenario, and 3.61% of EER and 17.44% of FNMR @1% at FMR in the mobile scenario, significantly improving previous state-of-the-art results. Concerning demographic fairness, the analyzed scores reflect the subjects’ age and gender to various extents, not negligible in a few cases. The framework runs on CodaLab 2 2 https://codalab.lisn.upsaclay.fr/competitions/14063 . . • We set up a novel framework for developing and evaluating keystroke biometrics. • We designed a unified experimental protocol with desktop and mobile scenarios. • We employ the biggest databases of keystroke dynamics, with over 185,000 subjects. • We provide a competitive performance baseline based on a limited-time challenge. • We provide a first exploration of the biometric fairness of keystroke dynamics.

Institute, Warsaw, Poland This paper describes the results of the IEEE BigData 2023 Keystroke Verification Challenge1(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 CodaLab2, 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.

Continuous Authentication (CA) using behavioural biometrics is a type of biometric identification that recognizes individuals based on their unique behavioural characteristics, like their typing style. However, the existing systems using keystroke or touch stroke data have limited accuracy and reliability. To improve this, smartphones’ Inertial Measurement Unit (IMU) sensors, which include accelerometers, gyroscopes, and magnetometers, can gather data on users’ behavioural patterns, such as how they hold their phones. Combining this IMU data with keystroke data can enhance the accuracy of behavioural biometrics-based CA. This paper proposes BehaveFormer, a new framework that employs keystroke and IMU data to create a reliable and accurate behavioural biometric CA system. It includes two Spatio-Temporal Dual Attention Transformers (STDAT), a novel transformer we introduce to extract more discriminative features from keystroke dynamics. Experimental results on three publicly available datasets (Aalto DB, HMOG DB, and HuMIdb) demonstrate that BehaveFormer outperforms the state-of-the-art behavioural biometric-based CA systems. For instance, BehaveFormer achieved an EER of 2.95% on the HuMIdb. Additionally, the proposed STDAT has been shown to improve the BehaveFormer system even when only keystroke data is used. For example, BehaveFormer achieved an EER of 1.80%. The code is available at https://github.com/DilshanSenarath/BehaveFormer.

Continuous Authentication (CA) is proposed as an alter-native authentication scheme for modern personal devices. Gait is a suitable biometric for CA due to its availability and low resource requirements. However, the drastic change in the gait pattern with changes in actions (such as walking, running or climbing stairs) and changes in terrain (such as walking on a flat surface or down an incline) makes it chal-lenging to deploy in a real-world CA system. We show that standard gait features are influenced by different actions. The gait pattern of an action is also in-fluenced by the actions performed before and immediately after. Therefore, gait features are usually not robust to these action variations. We propose action invariant gait features to address this robustness issue. Our proposed method learns action invariant gait features utilizing a Siamese Net-work architecture with triplet loss and a unique triplet mining protocol. Our evaluations highlight that our action in-variant features are robust to pre and post action impacts and real world action variations. These features allows for a CA system to be enrolled using a single action (walk) and be used across multiple different actions encountered throughout the day.

This paper describes the experimental framework and results of the IJCB 2022 Mobile Behavioral Biometrics Competition (MobileB2C). The aim of MobileB2C is bench-marking mobile user authentication systems based on behavioral biometric traits transparently acquired by mobile devices during ordinary Human-Computer Interaction (HCI), using a novel public database, BehavePassDB11https://github.com/BiDAlab/MobileB2C_BehavePassDE, and a standard experimental protocol. The competition is divided into four tasks corresponding to typical user activities: keystroke, text reading, gallery swiping, and tapping. The data are composed of touchscreen data and several background sensor data simultaneously acquired. “Random” (different users with different devices) and “skilled” (different user on the same device attempting to imitate the legitimate one) impostor scenarios are considered. The results achieved by the participants show the feasibility of user authentication through behavioral biometrics, although this proves to be a non-trivial challenge. MobileB2C will be established as an on-going competition22https://sites.google.com/view/mobileb2c/.

This paper presents a summary of the Masked Face Recognition Competitions (MFR) held within the 2021 International Joint Conference on Biometrics (IJCB 2021). The competition attracted a total of 10 participating teams with valid submissions. The affiliations of these teams are diverse and associated with academia and industry in nine different countries. These teams successfully submitted 18 valid solutions. The competition is designed to motivate solutions aiming at enhancing the face recognition accuracy of masked faces. Moreover, the competition considered the deployability of the proposed solutions by taking the compactness of the face recognition models into account. A private dataset representing a collaborative, multisession, real masked, capture scenario is used to evaluate the submitted solutions. In comparison to one of the topperforming academic face recognition solutions, 10 out of the 18 submitted solutions did score higher masked face verification accuracy.

Modern personal devices measure and store vast amounts of sensory data such as Inertial Measurement Unit (IMU) data. These on-body sensor data can be used as a biometric by observing human movement (gait). People are less cautious about privacy vulnerabilities of such sensory data. We highlight which personal characteristics can be derived from on-body sensor data and the effect of sensor location towards these privacy invasions. By analyzing sensor locations with respect to privacy and utility we discover sensor locations which preserve utility such as biometric authentication while reducing privacy vulnerability. We have collected (1) a multi-stream on-body IMU dataset using 3 IMU sensors, consisting of 6 sensor locations, 6 actions along with various physical, personality and socio-economic characteristics from 53 participants. (2) an opinion survey of the relative importance of each attribute from 566 participants. Using these datasets we show that gait data reveals a lot of personal information, which maybe a privacy concern. The opinion survey reveals a ranking of the physical characteristics based on the perceived importance. Using a privacy vulnerability index we show that sensors located in the front pocket/wrist are more privacy invasive compared to back-pocket/bag which are less privacy invasive without a significant loss of utility as a biometric.