Instituto de Telecomunicações

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Publications

Light field image coding with flexible viewpoint scalability and random access

Publication . Monteiro, Ricardo J. S.; Rodrigues, Nuno M. M.; Faria, Sérgio M. M.; Nunes, Paulo J. L.

This paper proposes a novel light field image compression approach with viewpoint scalability and random access functionalities. Although current state-of-the-art image coding algorithms for light fields already achieve high compression ratios, there is a lack of support for such functionalities, which are important for ensuring compatibility with different displays/capturing devices, enhanced user interaction and low decoding delay. The proposed solution enables various encoding profiles with different flexible viewpoint scalability and random access capabilities, depending on the application scenario. When compared to other state-of-the-art methods, the proposed approach consistently presents higher bitrate savings (44% on average), namely when compared to pseudo-video sequence coding approach based on HEVC. Moreover, the proposed scalable codec also outperforms MuLE and WaSP verification models, achieving average bitrate saving gains of 37% and 47%, respectively. The various flexible encoding profiles proposed add fine control to the image prediction dependencies, which allow to exploit the tradeoff between coding efficiency and the viewpoint random access, consequently, decreasing the maximum random access penalties that range from 0.60 to 0.15, for lenslet and HDCA light fields.

2021-05Journal article

Restricted access

The Digital Footprints on the Run: A Forensic Examination of Android Running Workout Applications

Publication . Nunes, Fabian; Domingues, Patricio; Frade, Miguel

This study applies a forensic examination to six distinct Android fitness applications centered around monitoring running activities. The applications are Adidas Running, MapMyWalk,Nike Run Club, Pumatrac, Runkeeper and Strava. Specifically, we perform a post mortem analysis of each application to find and document artifacts such as timelines and Global Positioning System (GPS) coordinates of running workouts that could prove helpful in digital forensic investigations. First, we focused on the Nike Run Club application and used the gained knowledge to analyze the other applications, taking advantage of their similarity. We began by creating a test environment and using each application during a fixed period. This procedure allowed us to gather testing data, and, to ensure access to all data generated by the apps, we used a rooted Android smartphone. For the forensic analysis, we examined the data stored by the smartphone application and documented the forensic artifacts found. To ease forensic data processing, we created several Python modules for the well-known Android Logs Events And Protobuf Parser (ALEAPP) digital forensic framework. These modules process the data sources, creating reports with the primary digital artifacts, which include the workout activities and related GPS data.

2024-08-23Journal article

Open access

LOSSY COMPRESSION OF BIOMEDICAL IMAGES FOR COMPUTER VISION ANALYSIS

Publication . Paulo, Edgar da Silva; Faria, Sérgio Manuel Maciel de; Távora, Luís Miguel de Oliveira Pegado de Noronha e; Thomaz, Lucas Arrabal

The exponential increase in medical and biomedical data acquisition is compelled by technological advances, namely in the imaging field. However, this exponential growth brings with it challenges in terms of processing capacity, transmission, and data storage. In response to this growing demand, increasingly efficient solutions have emerged, especially through computer vision for automatic image analysis and compression algorithms. This dissertation aims, on the one hand, to evaluate the performance of computer vision systems on previously compressed biomedical images. On the other hand, it increases the useful range of image variations, almost lossless and lossy, decreasing the impact of the change added by this method on the performance of computer vision algorithms in biomedical image analysis. In this sense, YOLO and Detectron2 are employed to evaluate the impact of coding distortion on their ability to detect mitochondria in electron microscopy images. The results of this study reveal that although the distortion introduced by compression affects their detection performance, it is negligible at lower compression ratios. Furthermore, two proposals are presented to improve the useful compression ratio, keeping the images characteristics that allow to perform the automatic detection of mitochondria. On the one hand, it is demonstrated that the proposed training methodology, which incorporates compressed versions of the original data during training, mitigates the impact of distortion on the performance of computer vision algorithms; on the other hand, allocating higher quality levels to regions of interest, compared to background elements, helps to sustain high performance at compression rates where computer vision algorithms typically start to lose effectiveness. These approaches allow the extension of the compression range with little impact on detection performance, thus contributing to the improvement of data processing, storage, and transmission in biomedical applications.

2024-06-14Master thesis

Open access

Fine-tuning pre-trained neural networks for medical image classification in small clinical datasets

Publication . Spolaôr, Newton; Lee, Huei Diana; Mendes, Ana Isabel; Nogueira, Conceição; Parmezan, Antonio Rafael Sabino; Takaki, Weber Shoity Resende; Coy, Claudio Saddy Rodrigues; Wu, Feng Chung; Fonseca-Pinto, Rui

Convolutional neural networks have been effective in several applications, arising as a promising supporting tool in a relevant Dermatology problem: skin cancer diagnosis. However, generalizing well can be difficult when little training data is available. The fine-tuning transfer learning strategy has been employed to differentiate properly malignant from non-malignant lesions in dermoscopic images. Fine-tuning a pre-trained network allows one to classify data in the target domain, occasionally with few images, using knowledge acquired in another domain. This work proposes eight fine-tuning settings based on convolutional networks previously trained on ImageNet that can be employed mainly in limited data samples to reduce overfitting risk. They differ on the architecture, the learning rate and the number of unfrozen layer blocks. We evaluated the settings in two public datasets with 104 and 200 dermoscopic images. By finding competitive configurations in small datasets, this paper illustrates that deep learning can be effective if one has only a few dozen malignant and non-malignant lesion images to study and differentiate in Dermatology. The proposal is also flexible and potentially useful for other domains. In fact, it performed satisfactorily in an assessment conducted in a larger dataset with 746 computerized tomographic images associated with the coronavirus disease.

2023-08-31Journal article

Open access

Digital Forensic Artifacts of FIDO2 Passkeys in Windows 11

Publication . Domingues, Patricio; Frade, Miguel; Negrão, Miguel

FIDO2’s passkey aims to provide a passwordless authentication solution. It relies on two main protocols – WebAuthn and CTAP2 – for authentication in computer systems, relieving users from the burden of using and managing passwords. FIDO2’s passkey leverages asymmetric cryptography to create a unique public/private key pair for website authentication. While the public key is kept at the website/application, the private key is created and stored on the authentication device designated as the authenticator. The authenticator can be the computer itself – same-device signing –, or another device – cross-device signing –, such as an Android smartphone that connects to the computer through a short-range communication method (NFC, Bluetooth). Authentication is performed by the user unlocking the authenticator device. In this paper, we report on the digital forensic artifacts left on Windows 11 systems by registering and using passkeys to authenticate on websites. We show that digital artifacts are created in Windows Registry and Windows Event Log. These artifacts enable the precise dating and timing of passkey registration, as well as the usage and identification of the websites on which they have been activated and utilized. We also identify digital artifacts created when Android smartphones are registered and used as authenticators in a Windows system. This can prove useful in detecting the existence of smartphones linked to a given individual.

2024-07-30Conference object

Open access