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- Exposing Manipulated Photos and Videos in Digital Forensics AnalysisPublication . Ferreira, Sara; Antunes, Mário; Correia, Manuel E.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.
- Usability of Smartbands by the Elderly Population in the Context of Ambient Assisted Living ApplicationsPublication . Correia, Luís; Fuentes, Daniel; Ribeiro, José; Costa, Nuno; Reis, Arsénio; Rabadão, Carlos; Barroso, João; Pereira, AntónioNowadays, the Portuguese population is aging at a fast pace. The situation is more severe in the interior regions of the country, where the rural areas have few people and have been constantly losing population; these are mostly elderly who, in some cases, live socially isolated. They are also often deprived of some types of social, health and technological services. One of the current challenges with respect to the elderly is that of improving the quality of life for those who still have some autonomy and live in their own residences so that they may continue living autonomously, while receiving the assistance of some exterior monitoring and supporting services. The Internet of Things (IoT) paradigm demonstrates great potential for creating technological solutions in this area as it aims to seamlessly integrate information technology with the daily lives of people. In this context, it is necessary to develop services that monitor the activity and health of the elderly in real time and alert caregivers or other family members in the case of an unusual event or behaviour. It is crucial that the technological system is able to collect data in a nonintrusive manner and without requiring much interaction with the elderly. Smartband devices are very good candidates for this purpose and, therefore, this work proposes assessing the level of acceptance of the usage of a smartbands by senior users in their daily activities. By using the definition of an architecture and the development of a prototype, it was possible to test the level of acceptance of smartbands by a sample of the elderly population—with surprising results from both the elderly and the caregivers—which constitutes an important contribution to the research field of Ambient Assisted Living (AAL). The evaluation showed that most users did not feel that the smartband was intrusive to their daily tasks and even considered using it in the future, while caregivers considered that the platform was very intuitive.
- A Survey on Data-driven Performance Tuning for Big Data Analytics PlatformsPublication . Costa, Rogério Luís de C.; Moreira, José; Pintor, Paulo; Santos, Veronica dos; Lifschitz, SérgioMany research works deal with big data platforms looking forward to data science and analytics. These are complex and usually distributed environments, composed of several systems and tools. As expected, there is a need for a closer look at performance issues. In this work, we review performance tuning strategies in the big data environment. We focus on data-driven tuning techniques, discussing the use of database inspired approaches. Concerning big data and NoSQL stores, performance tuning issues are quite different from the so-called conventional systems. Many existing solutions are mostly ad-hoc activities that do not fit for multiple situations. But there are some categories of data-driven solutions that can be taken as guidelines and incorporated into general-purpose auto-tuning modules for big data systems. We examine typical performance tuning actions, discussing available solutions to support some of the tuning process's primary activities. We also discuss recent implementations of data-driven performance tuning solutions for big data platforms. We propose an initial classification based on the domain state-of-the-art and present selected tuning actions for large-scale data processing systems. Finally, we organized existing works towards self-tuning big data systems based on this classification and presented general and system-specific tuning recommendations. We found that most of the literature pieces evaluate the use of tuning actions at the physical design perspective, and there is a lack of self-tuning machine-learning-based solutions for big data systems.
- A Dataset of Photos and Videos for Digital Forensics Analysis Using Machine Learning ProcessingPublication . Ferreira, Sara; Antunes, Mário; Correia, Manuel E.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.
- Microsoft's Your Phone environment from a digital forensic perspectivePublication . Domingues, Patricio; Andrade, Luis Miguel; Frade, MiguelYour Phone is a Microsoft dual mobile/desktop application that links a Windows 10 environment to a smartphone. The Android version provides the smartphone's user with the ability to control the mobile device from Windows 10, allowing to place/receive calls, send/receive text messages such as SMS, MMS and RCS, access up to the last 2000 photos/screenshots of the device and to receive notifications from applications, all through the Windows 10 Your Phone application and, if configured to do so, within Windows 10 notification center. This work analyzes the Your Phone environment, that is, Your Phone Companion for Android and Your Phone for Windows 10. The paper studies the digital forensic artifacts that can be found in a post mortem analysis, focusing on the SQLite3 databases used by both the Android and Windows 10 applications. We also compare the examined version with a previous version of Your Phone, showing that Your Phone newest functionalities bring new valuable artifacts for forensic examiners. The study shows that Your Phone data left on a Windows 10 device can be useful to access a copy of messages, photos, and document interactions, especially when the Android device is inaccessible or even physically unavailable. To ease the task for digital forensic examiners, we have updated our open-source YPA software that collects and analyzes Your Phone data from a Windows 10 system. YPA runs as a module within the digital forensic Autopsy software.
- SAR.IoT: Secured Augmented Reality for IoT Devices ManagementPublication . Fuentes, Daniel; Correia, Luís; Costa, Nuno; Reis, Arsénio; Barroso, João; Pereira, AntónioCurrently, solutions based on the Internet of Things (IoT) concept are increasingly being adopted in several fields, namely, industry, agriculture, and home automation. The costs associated with this type of equipment is reasonably small, as IoT devices usually do not have output peripherals to display information about their status (e.g., a screen or a printer), although they may have informative LEDs, which is sometimes insufficient. For most IoT devices, the price of a minimalist display, to output and display the device’s running status (i.e., what the device is doing), might cost much more than the actual IoT device. Occasionally, it might become necessary to visualize the IoT device output, making it necessary to find solutions to show the hardware output information in real time, without requiring extra equipment, only what the administrator usually has with them. In order to solve the above, a technological solution that allows for the visualization of IoT device information in actual time, using augmented reality and a simple smartphone, was developed and analyzed. In addition, the system created integrates a security layer, at the level of AR, to secure the shown data from unwanted eyes. The results of the tests carried out allowed us to validate the operation of the solution when accessing the information of the IoT devices, verify the operation of the security layer in AR, analyze the interaction between smartphones, the platform, and the devices, and check which AR markers are most optimized for this use case. This work results in a secure augmented reality solution, which can be used with a simple smartphone, to monitor/manage IoT devices in industrial, laboratory or research environments.
- IndoorCare: Low-Cost Elderly Activity Monitoring System through Image ProcessingPublication . Fuentes, Daniel; Correia, Luís; Costa, Nuno; Reis, Arsénio; Ribeiro, José; Rabadão, Carlos; Barroso, João; Pereira, AntónioThe Portuguese population is aging at an increasing rate, which introduces new problems, particularly in rural areas, where the population is small and widely spread throughout the territory. These people, mostly elderly, have low income and are often isolated and socially excluded. This work researches and proposes an affordable Ambient Assisted Living (AAL)‐based solution to monitor the activities of elderly individuals, inside their homes, in a pervasive and non-intrusive way, while preserving their privacy. The solution uses a set of low‐cost IoT sensor devices, computer vision algorithms and reasoning rules, to acquire data and recognize the activities performed by a subject inside a home. A conceptual architecture and a functional prototype were developed, the prototype being successfully tested in an environment similar to a real case scenario. The system and the underlying concept can be used as a building block for remote and distributed elderly care services, in which the elderly live autonomously in their homes, but have the attention of a caregiver when needed.
- Automatic Transcription of Polyphonic Piano Music Using Genetic Algorithms, Adaptive Spectral Envelope Modeling, and Dynamic Noise Level EstimationPublication . Reis, Gustavo; Fernandez de Vega, Francisco; Ferreira, AníbalThis paper presents a new method for multiple fundamental frequency (F0) estimation on piano recordings. We propose a framework based on a genetic algorithm in order to analyze the overlapping overtones and search for the most likely F0 combination. The search process is aided by adaptive spectral envelope modeling and dynamic noise level estimation: while the noise is dynamically estimated, the spectral envelope of previously recorded piano samples (internal database) is adapted in order to best match the piano played on the input signals and aid the search process for the most likely combination of F0s. For comparison, several state-of-the-art algorithms were run across various musical pieces played by different pianos and then compared using three different metrics. The proposed algorithm ranked first place on Hybrid Decay/Sustain Score metric, which has better correlation with the human hearing perception and ranked second place on both onset-only and onset–offset metrics. A previous genetic algorithm approach is also included in the comparison to show how the proposed system brings significant improvements on both quality of the results and computing time.
- A distributed multiagent system architecture for body area networks applied to healthcare monitoringPublication . Felisberto, Filipe; Laza, Rosalía; Fdez-Riverola, Florentino; Pereira, AntónioIn the last years the area of health monitoring has grown significantly, attracting the attention of both academia and commercial sectors. At the same time, the availability of new biomedical sensors and suitable network protocols has led to the appearance of a new generation of wireless sensor networks, the so-called wireless body area networks. Nowadays, these networks are routinely used for continuous monitoring of vital parameters, movement, and the surrounding environment of people, but the large volume of data generated in different locations represents a major obstacle for the appropriate design, development, and deployment of more elaborated intelligent systems. In this context, we present an open and distributed architecture based on a multiagent system for recognizing human movements, identifying human postures, and detecting harmful activities. The proposed system evolved from a single node for fall detection to a multisensor hardware solution capable of identifying unhampered falls and analyzing the users’ movement. The experiments carried out contemplate two different scenarios and demonstrate the accuracy of our proposal as a real distributed movement monitoring and accident detection system. Moreover, we also characterize its performance, enabling future analyses and comparisons with similar approaches.
- Digital forensic artifacts of the Your Phone application in Windows 10Publication . Domingues, Patrício; Frade, Miguel; Andrade, Luis Miguel; Silva, João VictorYour Phone is a Microsoft system that comprises two applications: a smartphone app for Android 7 + smartphones and a desktop application for Windows 10/18.03+. It allows users to access their most recent smartphone-stored photos/screenshots and send/receive short message service (SMS) and multimedia messaging service (MMS) within their Your Phone-linked Windows 10 personal computers. In this paper, we analyze the digital forensic artifacts created at Windows 10 personal computers whose users have the Your Phone system installed and activated. Our results show that besides the most recent 25 photos/screenshots and the content of the last 30-day of sent/received SMS/MMS, the contact database of the linked smartphone(s) is available in a accessible SQLite3 database kept at the Windows 10 system. This way, when the linked smartphone cannot be forensically analyzed, data gathered through the Your Phone artifacts may constitute a valuable digital forensic asset. Furthermore, to explore and export the main data of the Your Phone database as well as recoverable deleted data, a set of python scripts – Your Phone Analyzer (YPA) – is presented. YPA is available wrapped within an Autopsy module to assist digital practitioners to extract the main artifacts from the Your Phone system.