L’APP Mind Inclusion: la tecnologia assistiva per promuovere l’inclusione sociale delle persone con disabilità intelletive nella loro comunità

Information and Communication Technologies have been widely used to enhance evidencebased interventions in the education and training of individuals with intellectual disabilities. The use of Information and Communication Technologies for these purposes is called Assistive Technology. Assistive technology is able to support persons with intellectual disabilities to live fuller and richer lives in their communities, supporting more successful functioning across multiple domains: independent living and inclusion in community.This study has the aim to present and explain the development of an assistive technology tool for persons with intellectual disabilities, the Mind Inclusion APP which can allow persons with intellectual disabilities to search and reach for a location or an activity in their community. The APP was co-created through the support of a participatory design and a person centred approach.A sample of 48 people, including persons with disabilities, caregivers, educators and business owners, was involved at all stages of the project. This study has shown that persons with disabilities can interact better, be part of their society more easily, and learn new skills reducing the impact of disability on daily functioning by using the Mind Inclusion APP

Accesible co-creation tools for people with intellectual disabilities: working for and with end-users

[EN] In a world defined by rapid change, the search for solutions to societal challenges has become more complex calling for new paradigms of innovation focused on collaborations with the community and users. Cocreation approaches in the design and production of a service or product can bring low-cost innovation and unique and personalized customer experiences leading to user acceptance of a product or service. Under a co-creation perspective, the participatory approach developed in the MINDInclusion project aims to improve the inclusion of people with intellectual disabilities into public places and society by using a co-created online tool based on personal experiences of people with disabilities. Paying special attention to the Design thinking method, the main goal of this experience was to co-create cognitive accessible design tools that guide the collection of users and other stakeholders experiences in the process of defining problems and solutions. To this end, 14 researchers and educators worked defining together a set of guiding exercises and design thinking methods for the 4 co-design cycles. As a result two tools were developed to gather information to recreate as a final output “personas scenarios”, an “empathy map” and expected “use scenarios”. The former was an adapted game board about public places based on the traditional monopoly game and the latter a diary with a set of activities that will facilitate the collection of contextual information. Previous experiences have shown that co-design process can promote greater social cohesion, acceptance and empowerment. Working with people with intellectual disability presents several challenges since the co-creation process needs to be cognitive accessible. However, the tools created under this experience can be extrapolated to other contextsAlmeida, R.; Losada Durán, R.; Cid Bartolomé, T.; Giaretta, A.; Segalina, A.; Bessegato, A.; Visentin, S.... (2020). Accesible co-creation tools for people with intellectual disabilities: working for and with end-users. Editorial Universitat Politècnica de València. 53-61. https://doi.org/10.4995/INN2019.2019.10086OCS536

GSWO: A Programming Model for GPU-enabled Parallelization of Sliding Window Operations in Image Processing

Sliding Window Operations (SWOs) are widely used in image processing applications. They often have to be performed repeatedly across the target image, which can demand significant computing resources when processing large images with large windows. In applications in which real-time performance is essential, running these filters on a CPU often fails to deliver results within an acceptable timeframe. The emergence of sophisticated graphic processing units (GPUs) presents an opportunity to address this challenge. However, GPU programming requires a steep learning curve and is error-prone for novices, so the availability of a tool that can produce a GPU implementation automatically from the original CPU source code can provide an attractive means by which the GPU power can be harnessed effectively. This paper presents a GPUenabled programming model, called GSWO, which can assist GPU novices by converting their SWO-based image processing applications from the original C/C++ source code to CUDA code in a highly automated manner. This model includes a new set of simple SWO pragmas to generate GPU kernels and to support effective GPU memory management. We have implemented this programming model based on a CPU-to-GPU translator (C2GPU). Evaluations have been performed on a number of typical SWO image filters and applications. The experimental results show that the GSWO model is capable of efficiently accelerating these applications, with improved applicability and a speed-up of performance compared to several leading CPU-to- GPU source-to-source translators

Ga-based robustness for evaluation method for digital image watermarking

2008-11-05Sardegna Ricerche, Edificio 2, Località Piscinamanna 09010 Pula (CA) - ItaliaSeconda giornata della sicurezza informatica in Sardegna. Premio Akhela Pattern Recognition per la Sicurezza Informatic

Active and Passive Multimedia Forensics

Thanks to their huge expressive capability, coupled with the widespread use of the Internet and of affordable and high quality cameras and computers, digital multimedia represent nowadays one of the principal means of communication. Besides the many benefits, the wide proliferation of such contents has lead to problematic issues regarding their authen- ticity and security. To cope with such problems, the scientific community has focused its attention on digital forensic techniques. The objective of this doctoral study is to actively contribute to this field of research, developing efficient techniques to protect digital contents and verify their integrity. Digital Watermarking has been initially proposed as a valuable instrument to prove con- tent ownership, protect copyright and verify integrity, by imperceptibly embedding a mes- sage into a documents. Such message can later be detected and used to disclose possible copyrights violations or manipulations. For specific applications, such as copyright pro- tection, the watermark is required to be as robust as possible, surviving possible attack a malevolent user may be willing to apply. In light of this, we developed a novel watermark- ing benchmarking tool able to evaluate the robustness of watermarking techniques under the attack of multiple processing operators. On the other hand, for specific applications, such as forensic and medical, the robustness requirement is overtaken by integrity preser- vation. To cope with this aim, fragile watermarking has been developed, assuming that the watermark is modified whenever a tampering occurs, thus its absence can be taken as ev- idence of manipulation. Among this class of techniques, we developed a prediction-based reversible watermarking algorithm, which allows a perfect recovery of both the original content and the watermark. More recently, passive forensics approaches, which work in absence of any watermark or special hardware, have been proposed for authentication purposes. The basic idea is that the manipulation of a digital media, if performed properly, may not leave any visual trace of its occurrence, but it alters the statistics of the content. Without any prior knowledge about the content, such alterations can be revealed and taken as evidence of forgery. We focused our study on geometric-based forensic techniques both for images and videos au- thentication. Firstly we proposed a method for authenticating text on signs and billboards, based on the assumption that text on a planar surface is imaged under perspective projec- tion, but it is unlikely to satisfy such geometric mapping when manipulated. Finally, we proposed a novel geometric technique to detect physically implausible trajectories of ob- jects in video sequences. This technique explicitly models the three-dimensional trajectory of objects in free-flight and the corresponding two-dimensional projection into the image plane. Deviations from this model provide evidence of manipulation

Exposing digital forgeries in ballistic motion

We describe a geometric technique to detect physically implausible trajectories of objects in video sequences. This technique explicitly models the three-dimensional ballistic motion of objects in free-flight and the two-dimensional projection of the trajectory into the image plane of a static or moving camera. Deviations from this model provide evidence of manipulation. The technique assumes that the object's trajectory is substantially influenced only by gravity, that the image of the object's center of mass can be determined from the images, and requires that any camera motion can be estimated from background elements. The computational requirements of the algorithm are modest, and any detected inconsistencies can be illustrated in an intuitive, geometric fashion. We demonstrate the efficacy of this analysis on videos of our own creation and on videos obtained from video-sharing websites. © 2011 IEEE

DETECTING PHOTO MANIPULATION ON SIGNS AND BILLBOARDS

The manipulation of text on a sign or billboard is relatively easy to do in a way that is perceptually convincing. When text is on a planar surface and imaged under perspective projection, the text undergoes a specific distortion. When text is manipulated, it is unlikely to precisely satisfy this geometric mapping. We describe a technique for detecting if text in an image obeys the expected perspective projection, deviations from which are used as evidence of tampering. Index Terms — Digital Forensics, Digital Tampering 1

Bad teacher or unruly student: Can deep learning say something in Image Forensics analysis?

The pervasive availability of the Internet, coupled with the development of increasingly powerful technologies, has led digital images to be the primary source of visual information in nowadays society. However, their reliability as a true representation of reality cannot be taken for granted, due to the affordable powerful graphics editing softwares that can easily alter the original content, leaving no visual trace of any modification on the image making them potentially dangerous. This motivates developing technological solutions able to detect media manipulations without a prior knowledge or extra information regarding the given image. At the same time, the huge amount of available data has also led to tremendous advances of data-hungry learning models, which have already demonstrated in last few years to be successful in image classification. In this work we propose a deep learning approach for tampered image classification. To our best knowledge, this the first attempt to use the deep learning paradigm in an image forensic scenario. In particular, we propose a new blind deep learning approach based on Convolutional Neural Networks (CNN) able to learn invisible discriminative artifacts from manipulated images that can be exploited to automatically discriminate between forged and authentic images. The proposed approach not only detects forged images but it can be extended to localize the tampered regions within the image. This method outperforms the state-of-the-art in terms of accuracy on CASIA TIDE v2.0 dataset. The capability of automatically crafting discriminant features can lead to surprising results. For instance, detecting image compression filters used to create the dataset. This argument is also discussed within this paper