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    • Deep learning for compressive sensing: a ubiquitous systems perspective
    Authors: Alina L. Machidon, Machidon; Veljko, Pejović;  Advisor: -;  Co-Author: - (2023)

    Compressive sensing (CS) is a mathematically elegant tool for reducing the sensor sampling rate, potentially bringing context-awareness to a wider range of devices. Nevertheless, practical issues with the sampling and reconstruction algorithms prevent further proliferation of CS in real world domains, especially among heterogeneous ubiquitous devices. Deep learning (DL) naturally complements CS for adapting the sampling matrix, reconstructing the signal, and learning from the compressed samples. While the CS–DL integration has received substantial research interest recently, it has not yet been thoroughly surveyed, nor has any light been shed on practical issues towards bringing the CS–DL to real world implementations in the ubiquitous computing domain.
    • Image forgery detection a survey of recent deep-learning approaches
    Authors: Marcello, Zanardelli; Fabrizio, Guerrini; Riccardo, Leonardi;  Advisor: -;  Co-Author: - (2022)

    In the last years, due to the availability and easy of use of image editing tools, a large amount of fake and altered images have been produced and spread through the media and the Web. A lot of different approaches have been proposed in order to assess the authenticity of an image and in some cases to localize the altered (forged) areas. In this paper, we conduct a survey of some of the most recent image forgery detection methods that are specifically designed upon Deep Learning (DL) techniques, focusing on commonly found copy-move and splicing attacks. DeepFake generated content is also addressed insofar as its application is aimed at images, achieving the same effect as splicing. This survey is especially timely because deep learning powered techniques appear to be the most relev...