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About

About

Professor Manuel E. Correia got his MSc in foundations of advanced information processing technologies from the Imperial College of London in 1992 and his PhD in Computer Science from Oporto University in 2001. He is currently an Associate Professor at the Department of Computer Science of the Faculty of Science of Oporto University and a researcher in the field of computer security at the CRACS group of INESC TEC Porto where he is responsible for research projects related to anomaly detection and identity management and the security aspects of several industry contracts. He has also been a consultant for some Portuguese public agencies (Health and Education) in computer security. He co-founded a spin-off from the University, called HealthySystems that centers its activity in the area of information security with a strong focus on auditing, eID and anonymization techniques. In 2014 this spin-off won the National Pharmacies Association Prize for the pharmacy of the future with a project called DigitalPharma and more recently a CIO Summit Portugal award prize with a software solution developed to support clinical management and integration for large Hospitals.

Interest
Topics
Details

Details

  • Name

    Manuel Eduardo Correia
  • Cluster

    Computer Science
  • Role

    Senior Researcher
  • Since

    01st January 2009
004
Publications

2023

Deterministic or Probabilistic? - A Survey on Byzantine Fault Tolerant State Machine Replication

Authors
Freitas, T; Soares, J; Correia, ME; Martins, R;

Publication
Comput. Secur.

Abstract

2022

Digital Forensics for the Detection of Deepfake Image Manipulations

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

Publication
ERCIM NEWS

Abstract

2022

A Decentralised Real Estate Transfer Verification Based on Self-Sovereign Identity and Smart Contracts

Authors
Shehu, AS; Pinto, A; Correia, ME;

Publication
SECRYPT : PROCEEDINGS OF THE 19TH INTERNATIONAL CONFERENCE ON SECURITY AND CRYPTOGRAPHY

Abstract

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

A Dataset of Photos and Videos for Digital Forensics Analysis Using Machine Learning Processing

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

Publication
DATA

Abstract
Deepfake and manipulated digital photos and videos are being increasingly used in a myriad of cybercrimes. Ransomware, the dissemination of fake news, and digital kidnapping-related crimes are the most recurrent, in which tampered multimedia content has been the primordial disseminating vehicle. Digital forensic analysis tools are being widely used by criminal investigations to automate the identification of digital evidence in seized electronic equipment. The number of files to be processed and the complexity of the crimes under analysis have highlighted the need to employ efficient digital forensics techniques grounded on state-of-the-art technologies. Machine Learning (ML) researchers have been challenged to apply techniques and methods to improve the automatic detection of manipulated multimedia content. However, the implementation of such methods have not yet been massively incorporated into digital forensic tools, mostly due to the lack of realistic and well-structured datasets of photos and videos. The diversity and richness of the datasets are crucial to benchmark the ML models and to evaluate their appropriateness to be applied in real-world digital forensics applications. An example is the development of third-party modules for the widely used Autopsy digital forensic application. This paper presents a dataset obtained by extracting a set of simple features from genuine and manipulated photos and videos, which are part of state-of-the-art existing datasets. The resulting dataset is balanced, and each entry comprises a label and a vector of numeric values corresponding to the features extracted through a Discrete Fourier Transform (DFT). The dataset is available in a GitHub repository, and the total amount of photos and video frames is 40,588 and 12,400, respectively. The dataset was validated and benchmarked with deep learning Convolutional Neural Networks (CNN) and Support Vector Machines (SVM) methods; however, a plethora of other existing ones can be applied. Generically, the results show a better F1-score for CNN when comparing with SVM, both for photos and videos processing. CNN achieved an F1-score of 0.9968 and 0.8415 for photos and videos, respectively. Regarding SVM, the results obtained with 5-fold cross-validation are 0.9953 and 0.7955, respectively, for photos and videos processing. A set of methods written in Python is available for the researchers, namely to preprocess and extract the features from the original photos and videos files and to build the training and testing sets. Additional methods are also available to convert the original PKL files into CSV and TXT, which gives more flexibility for the ML researchers to use the dataset on existing ML frameworks and tools.

Supervised
thesis

2022

Extensão web para deteção de conteúdo multimédia manipulado

Author
Diana Esteves Carvalho D'Egas

Institution
UP-FCUP

2022

Optimized Detector of Manipulated Media Content

Author
David Miguel dos Santos Maia

Institution
UP-FCUP

2022

SkyNet: Towards a Dynamic and Adaptive Intrusion Tolerant System

Author
Tadeu Augusto Leite Freitas

Institution
UP-FCUP

2022

Infrastructure for Identity Management, Authentication and Authorization

Author
Muhammad Shehu Abubakar-Sadiq

Institution
UTAD

2021

A machine learning based digital forensics application to detect tampered multimedia files

Author
Sara Cardoso Ferreira

Institution
UP-FCUP