Publications related to On the Information in Deep Biometric Templates: from Vulnerability of Unprotected Templates to Leakage in Protected Templates

Related publications (11)

Emotion information recovery potential of wav2vec2 network fine-tuned for speech recognition task

Fine-tuning has become a norm to achieve state-of-the-art performance when employing pre-trained networks like foundation models. These models are typically pre-trained on large-scale unannotated data using self-supervised learning (SSL) methods. The SSL-b ...

Institute of Electrical and Electronics Engineers Inc.2025

Adversarially Robust CLIP Models Can Induce Better (Robust) Perceptual Metrics

Francesco Croce

Measuring perceptual similarity is a key tool in computer vision. In recent years perceptual metrics based on features extracted from neural networks with large and diverse training sets, e.g. CLIP, have become popular. At the same time, the metrics extrac ...

IEEE2025

On the Conflict Between Robustness and Learning in Collaborative Machine Learning

Mathilde Aliénor Raynal

Collaborative Machine Learning (CML) allows participants to jointly train a machine learning model while keeping their training data private. In many scenarios where CML is seen as the solution to privacy issues, such as health-related applications, safety ...

IEEE2025

Meteorological information prediction using images from webcams

Demetri Psaltis, Christophe Moser, Luisa Lambertini, Roy Sarkis

One aspect of the present invention proposes a method of predicting meteorological information by using an artificial deep neural network. The present invention according to one example focuses on image processing, specifically on fusing a plurality of ima ...

2025

Automated processing of eXplainable Artificial Intelligence outputs in deep learning models for fault diagnostics of large infrastructures

Olga Fink

Deep Learning (DL) models processing images to recognize the health state of large infrastructure components can exhibit biases and rely on non-causal shortcuts. eXplainable Artificial Intelligence (XAI) can address these issues but manually analyzing expl ...

2025

Machine learning-aided hysteretic response prediction of double skin composite wall under earthquake loads

Yongtao Wu

Accurately and rapidly simulating the hysteretic behavior of double skin composite wall (DSCW) under earthquake loads enhance the efficiency of seismic performance assessments for high-rise steel-concrete composite structures. The application of artificial ...

2025

Second Edition Frcsyn Challenge at Cvpr 2024: Face Recognition Challenge in the Era of Synthetic Data

Alexander Carl Unnervik

Synthetic data is gaining increasing relevance for training machine learning models. This is mainly motivated due to several factors such as the lack of real data and intra-class variability, time and errors produced in manual labeling, and in some cases p ...

IEEE2024

Anonymization: The imperfect science of using data while preserving privacy

Ana-Maria Cretu

Information about us, our actions, and our preferences is created at scale through surveys or scientific studies or as a result of our interaction with digital devices such as smartphones and fitness trackers. The ability to safely share and analyze such d ...

AMER ASSOC ADVANCEMENT SCIENCE2024

VIVALDI: steel billet tracking with advanced computer vision

Liza Carol Andrea Dunbar

Our advanced computer vision system allows for the precise tracking of serial numbers on steel billets in challenging industrial settings. It combines cutting-edge hardware and machine learning, excelling in character recognition (99.8%) and localization w ...

SPIE2024

Self-Supervised Underwater Caustics Removal and Descattering via Deep Monocular SLAM

Devis Tuia, Jonathan Paul Sauder

Underwater scenes are challenging for computer vision methods due to color degradation caused by the water column and detrimental lighting effects such as caustic caused by sunlight refracting on a wavy surface. These challenges impede widespread use of co ...

Springer Nature (Switzerland)2024

Graph-based approximate message passing iterations

Raphaël Jean Berthier

Approximate message passing (AMP) algorithms have become an important element of high-dimensional statistical inference, mostly due to their adaptability and concentration properties, the state evolution (SE) equations. This is demonstrated by the growing ...

Oxford University Press2023