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Publications

Publications by CSE

2021

Exposing Manipulated Photos and Videos in Digital Forensics Analysis

Authors
Ferreira, S; Antunes, M; Correia, ME;

Publication
JOURNAL OF IMAGING

Abstract
Tampered multimedia content is being increasingly used in a broad range of cybercrime activities. The spread of fake news, misinformation, digital kidnapping, and ransomware-related crimes are amongst the most recurrent crimes in which manipulated digital photos and videos are the perpetrating and disseminating medium. Criminal investigation has been challenged in applying machine learning techniques to automatically distinguish between fake and genuine seized photos and videos. Despite the pertinent need for manual validation, easy-to-use platforms for digital forensics are essential to automate and facilitate the detection of tampered content and to help criminal investigators with their work. This paper presents a machine learning Support Vector Machines (SVM) based method to distinguish between genuine and fake multimedia files, namely digital photos and videos, which may indicate the presence of deepfake content. The method was implemented in Python and integrated as new modules in the widely used digital forensics application Autopsy. The implemented approach extracts a set of simple features resulting from the application of a Discrete Fourier Transform (DFT) to digital photos and video frames. The model was evaluated with a large dataset of classified multimedia files containing both legitimate and fake photos and frames extracted from videos. Regarding deepfake detection in videos, the Celeb-DFv1 dataset was used, featuring 590 original videos collected from YouTube, and covering different subjects. The results obtained with the 5-fold cross-validation outperformed those SVM-based methods documented in the literature, by achieving an average F1-score of 99.53%, 79.55%, and 89.10%, respectively for photos, videos, and a mixture of both types of content. A benchmark with state-of-the-art methods was also done, by comparing the proposed SVM method with deep learning approaches, namely Convolutional Neural Networks (CNN). Despite CNN having outperformed the proposed DFT-SVM compound method, the competitiveness of the results attained by DFT-SVM and the substantially reduced processing time make it appropriate to be implemented and embedded into Autopsy modules, by predicting the level of fakeness calculated for each analyzed multimedia file.

2021

Extracting neuronal activity signals from microscopy recordings of contractile tissue using B-spline Explicit Active Surfaces (BEAS) cell tracking

Authors
Kazwiny, Y; Pedrosa, J; Zhang, ZQ; Boesmans, W; D'hooge, J; Vanden Berghe, P;

Publication
SCIENTIFIC REPORTS

Abstract
Ca2+ imaging is a widely used microscopy technique to simultaneously study cellular activity in multiple cells. The desired information consists of cell-specific time series of pixel intensity values, in which the fluorescence intensity represents cellular activity. For static scenes, cellular signal extraction is straightforward, however multiple analysis challenges are present in recordings of contractile tissues, like those of the enteric nervous system (ENS). This layer of critical neurons, embedded within the muscle layers of the gut wall, shows optical overlap between neighboring neurons, intensity changes due to cell activity, and constant movement. These challenges reduce the applicability of classical segmentation techniques and traditional stack alignment and regions-of-interest (ROIs) selection workflows. Therefore, a signal extraction method capable of dealing with moving cells and is insensitive to large intensity changes in consecutive frames is needed. Here we propose a b-spline active contour method to delineate and track neuronal cell bodies based on local and global energy terms. We develop both a single as well as a double-contour approach. The latter takes advantage of the appearance of GCaMP expressing cells, and tracks the nucleus' boundaries together with the cytoplasmic contour, providing a stable delineation of neighboring, overlapping cells despite movement and intensity changes. The tracked contours can also serve as landmarks to relocate additional and manually-selected ROIs. This improves the total yield of efficacious cell tracking and allows signal extraction from other cell compartments like neuronal processes. Compared to manual delineation and other segmentation methods, the proposed method can track cells during large tissue deformations and high-intensity changes such as during neuronal firing events, while preserving the shape of the extracted Ca2+ signal. The analysis package represents a significant improvement to available Ca2+ imaging analysis workflows for ENS recordings and other systems where movement challenges traditional Ca2+ signal extraction workflows.

2021

Deformation Fringes Detection in SAR interferograms Using Deep Learning

Authors
Silva, B; Sousa, JJ; Lazecky, M; Cunha, A;

Publication
Procedia Computer Science

Abstract
The success achieved by using SAR data in the study of the Earth led to a firm commitment from space agencies to develop more and better space-borne SAR sensors. This involvement of the space agencies makes us believe that it is possible to increase the potential of SAR interferometry (InSAR) to near real-time monitoring. Among this ever-increasing number of sensors, the ESA's Sentinel-1 (C-band) mission stands out and appears to be disruptive. This mission is acquiring vast volumes of data making current analyzing approaches inviable. This amount of data can no longer be analyzed and studied using classic methods raising the need to use and create new techniques. We believe that Machine Learning techniques can be the solution to overcome this issue since they allow to train Deep Learning models to automate human processes for a vast volume of data. In this paper, we use deep learning models to automatically find and locate deformation areas in InSAR interferograms without atmospheric correction. We train three state-of-the-art classification models for detection deformation areas, achieving an AUC of 0.864 for the best model (VGG19 for wrapped interferograms). Additionally, we use the same models as encoders to train U-net models, achieving a Dice score of 0.54 for InceptionV3. It is necessary more data to achieve better results in segmentation.

2021

FGPE Gamification Service: A GraphQL Service to Gamify Online Education

Authors
Paiva, JC; Haraszczuk, A; Queirós, R; Leal, JP; Swacha, J; Kosta, S;

Publication
TRENDS AND APPLICATIONS IN INFORMATION SYSTEMS AND TECHNOLOGIES, VOL 4

Abstract
Keeping students engaged while learning programming is becoming more and more imperative. Of the several proposed techniques, gamification is presumably the most widely studied and has already proven as an effective means to engage students. However, there is a complete lack of public and customizable solutions to gamified programming education that can be reused with personalized rules and learning material. FGPE Gamification Service (FGPE GS) is an open-source GraphQL service that transforms a package containing the gamification layer – adhering to a dedicated open-source language, GEdIL – into a game. The game provides students with a gamified experience leveraging on the automatically-assessable activities referenced by the challenges. This paper presents FGPE GS, its architecture, data model, and validation. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

2021

Automatic detection of Flavescense Dorée grapevine disease in hyperspectral images using machine learning

Authors
Silva, DM; Bernardin, T; Fanton, K; Nepaul, R; Pádua, L; Sousa, JJ; Cunha, A;

Publication
Procedia Computer Science

Abstract
The technological revolution that we have been witnessing recently has allowed components miniaturization and made electronic components accessible. Hyperspectral sensors benefited from these advances and could be mounted on unmanned aerial vehicles, which was unthinkable until recently. This fact significantly increased the applications of hyperspectral data, namely in agriculture, especially in the detection of diseases at an early stage. The vineyard is one of the agricultural sectors that has the most to gain from the use of this type of data, both by the economic value and by the number of diseases the plants are exposed to. The Flavescense dorée is a disease that attacks vineyards and may conduct to a significant loss. Nowadays, the detection of this disease is based on the visual identification of symptoms performed by experts who cover the entire area. However, this work remains tedious and relies only on the human eye, which is a problem since sometimes healthy plants are torn out, while diseased ones are left. If the experts think they have found symptoms, they take samples to send to the laboratory for further analysis. If the test is positive, then the whole vine is uprooted, to limit the spread of the disease. In this context, the use of hyperspectral data will allow the development of new disease detection methods. However, it will be necessary to reduce the volume of data used to make them usable by conventional resources. Fortunately, the advent of machine learning techniques empowered the development of systems that allow better decisions to be made, and consequently save time and money. In this article, a machine learning approach, which is based on an Autoencoder to automatically detect wine disease, is proposed.

2021

Foreign Language Learning Gamification Using Virtual Reality-A Systematic Review of Empirical Research

Authors
Pinto, RD; Peixoto, B; Melo, M; Cabral, L; Bessa, M;

Publication
EDUCATION SCIENCES

Abstract
Virtual reality has shown to have great potential as an educational tool when it comes to new learning methods. With the growth and dissemination of this technology, there is a massive opportunity for teachers to add this technology to their methods of teaching a second/foreign language, since students keep showing a growing interest in new technologies. This systematic review of empirical research aims at understanding whether the use of gaming strategies in virtual reality is beneficial for the learning of a second/foreign language or not. Results show that more than half of the articles proved that virtual reality technologies with gaming strategies can be used to learn a foreign language. It was also found that "learning" was the most evaluated dependent variable among the chosen records, augmented reality was the leading technology used, primary education and lower secondary was the most researched school stages, and the most used language to evaluate the use of gamified technology was by far the English language. Given the lack of directed investigation, it is recommended to use these technologies to support second language learning and not entirely replace traditional approaches. A research agenda is also proposed by the authors.

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