Abstract
This paper presents the main contributions of the VCMI Team to the ImageCLEFmedical GANs 2023 task. This task aims to evaluate whether synthetic medical images generated using Generative Adversarial Networks (GANs) contain identifiable characteristics of the training data. We propose various approaches to classify a set of real images as having been used or not used in the training of the model that generated a set of synthetic images. We use similarity-based approaches to classify the real images based on their similarity to the generated ones. We develop autoencoders to classify the images through outlier detection techniques. Finally, we develop patch-based methods that operate on patches extracted from real and generated images to measure their similarity. On the development dataset, we attained an F1-score of 0.846 and an accuracy of 0.850 using an autoencoder-based method. On the test dataset, a similarity-based approach achieved the best results, with an F1-score of 0.801 and an accuracy of 0.810. The empirical results support the hypothesis that medical data generated using deep generative models trained without privacy constraints threatens the privacy of patients in the training data.

Abstract

Abstract
An optical fiber sensor for hydrogen detection is presented. It is based on processed fiber Bragg gratings coated with palladium thin films where its expansion due to the hydrogen adsorption is monitored as strain measurements. © Optica Publishing Group 2023, © 2023 The Author(s)

Abstract

Abstract

Abstract
Cryptographic ransomware attacks are constantly evolving by obfuscating their distinctive features (e.g., I/O patterns) to bypass detection mechanisms and to run unnoticed at infected servers. Thus, efficiently exploring the I/O behavior of ransomware families is crucial so that security analysts and engineers can better understand these and, with such knowledge, enhance existing detection methods. In this paper, we propose CRIBA, an open-source framework that simplifies the exploration, analysis, and comparison of I/O patterns for Linux cryptographic ransomware. Our solution combines the collection of comprehensive information about system calls issued by ransomware samples, with a customizable and automated analysis and visualization pipeline, including tailored correlation algorithms and visualizations. Our study, including 5 Linux ransomware families, shows that CRIBA provides comprehensive insights about the I/O patterns of these attacks while aiding in exploring common and differentiating traits across families.