The goal of the REVEAL project is to address the problem of digital image forensics and to provide reference algorithms to the academic community and to the forensics experts. We will tackle the problems of image tempering detection, image tempering localization and large scale image analysis, which are the main requirements of the Defals challenge. In order to do so, our methodology relies on several core components: 1. the acquisition of a large image database that will be used in order to train models designed for forensics purposes; this database will also be automatically processed in order to generate examples of manipulated images, 2. the development of forensics algorithms that will be able to scale with respect to the large diversity of image sources and image numbers. This will be possible by specific coding principles and the access to HPCs. We plan also to release our algorithms to the academic community by the development of a dedicated platform, but also to release a forensic private server especially dedicated to forensics experts, 3. the diversification of the forensics strategies that will be developed, some relying on feature extractions, other relying on the training of statistical models, some possibly tutored by forensics experts, other fully automatic using deep learning strategies, some supervised, other unsupervised, . . . 4. the strong commitment of the academic researchers at participating at the two milestones of the Defals challenge, particularly the project leader who has already been confronted to the organization and the participation in two challenges in data-hiding, and the two other partners who have participated to previous challenges in data-hiding, 5. the guaranty to have a continuous transfer between the two academic partners (CRIStAL and GIPSA) and the technological institute B-COM in order to develop products dedicated to forensics experts, 6. the participation members of the DxO company as scientific advisors in the scientific discussions and the different developments of the project, DxO is one leading company in image development and photographic device characterization. The REVEAL project is naturally decomposed into tasks that will lead to the development of state of the art algorithms in forensics, and to the participation in the contest. Task T0 (Coordination and communication) is the general task of the project. The aim of this task is to ensure the smooth running of the project, but also to deal with internal and external communication subtasks. Then, three tasks are dedicated to research. The first one (T1) aims at building very large datasets of original and manipulated images. The two next ones (T2 and T3) respectively address the two core parts of the research project which are the development of forensics tools that are either based on feature extraction algorithms, or on statistical models. The goal of these two tasks will be to provide state-of-the-art methods for the Defals contest. This is achieved by the conduction of a dedicated task on participating in the two Defals submissions (T4). Another output of Defals, linked with T4, is the generation of two forensics platforms, one open for academics and for DGA, and an another dedicated to forensics experts.

Information access on the Internet is exploding. Usage is shifting to multimedia applications, social networking and IoE. Cellular networks are moving to the next generation. Networking technology is shifting towards virtualization, with SDN and NFV likely to change the infrastructure landscape. The cloud concept is transforming the Internet to a network of data centers, with a communication model consisting of computer-to-cloud-to-computer interactions. Security concerns are leading to an encryption of all traffic, wreaking havoc with established network mechanisms. In this scenario with dramatic growth and evolution, where abstractions and interfaces become fundamental, ICN is just the perfect solution. ICN2020 will build on the wealth of studies performed on ICN with six main aims: a) design and develop a set of innovative applications such as video delivery, interactive videos and social networks to exploit ICN; b) augment ICN with IoT features and cloud/CDN/virtualization services; c) accordingly enhance existing ICN solutions/architectures; d) build local and global test-bed(s) to experiment the applications, services and ICN enhancements; e) contribute to common APIs and standards, by continuing the work that project partners are already doing; and f) Industry POCs of products and services exploiting ICN. The ICN2020 consortium includes leading experts in ICN and contributors to ICN testbeds in EU, Japan and USA, thus making the goal of federating them a credible one. Partners are also coordinators of running projects on 5G and Cloud topics, allowing fruitful cooperation with fellow projects of the EU-JP1, EU-JP2 calls and increasing the overall expected impact of the EU-Japan cooperation. In a time when 5G networks are being designed, with foreseen unprecedented flexibility due to the virtualization and slice concepts, the development of compelling demonstrations of ICN for real-world use-cases will encourage critical industry investment of resources.

MAIA’s vision is that, within 5-10 years, CCAM and UAM based services will be available in an integrated manner with the airport infrastructure and the remaining access modes. AI tools and digital twins will be used for anticipating the impacts of different service configurations on the performance of airports as multimodal hubs. Aviation stakeholders will be able to make evidence-based decisions on how to implement, operate and regulate novel services in a way they improve passenger experience, increase capacity and help reach sustainability goals. The goal of MAIA is to develop a set of data analytics and modelling tools to support the evidence-based design and implementation of multimodal airport access solutions based on two passenger mobility innovations: shared autonomous vehicle fleets and unmanned aerial vehicle fleets. MAIA tools will monitor and anticipate passenger behaviour changes due to these new options, optimise vehicle dispatching under multimodal disruptions and recommend appropriate locations for vertiports, with the aim of maximising the contribution of these mobility innovations to the competitiveness and sustainability of the European aviation sector. The specific objectives of MAIA are (i) identify the opportunities and risks associated with passenger mobility innovations in a multimodal airport access context; (ii) develop MAIA-Engine, a toolset for a passenger-centric design and implementation of innovative multimodal airport access services, which includes new methods and tools for predicting passenger behaviour; (iii) develop MAIA-CCAM, a vehicle dispatching tool to support the operation of Shared Autonomous Vehicle (SAV) fleets in the airport access, able to mitigate multimodal disruption impacts; (iv) develop MAIA-UAM, a vertiport site selection framework to support the implementation of Unmanned Aerial Vehicles (UAV) services in the airport access, able to balance passenger experience, capacity and environmental sustainability.

Rapidly evolving digital technologies such as the IoT, cloud and AI overrun classical industries, such as automotive, which have longer innovation and development cycles. The current trend of interconnecting cars with local infrastructure and cloud backends opens large potentials for data-driven applications, enhanced user experience, and new business models but also needs to consider privacy of the users inside the vehicle and others, just observed in the streets. This becomes especially critical with respect to GDPR. Goal of AUTOPSY is to create a better understanding of the data flows in automotive environments in the light of GDPR and create a privacy-aware system model for an automotive use-case to address various aspects of GDPR in specific technical designs. The technology of tainting will be applied to separate communication streams between the sensor and multiple parties accessing and processing the data with different privileges. AUTOPSY aims to design a dynamic and scalable end to end infrastructure that protects the data with lightweight privacy preserving techniques onboard the vehicle. Across the expertise of the different partners, the practical feasibility is demonstrated by modifying a resource constrained TCU with an implementation of the privacy-preserving techniques and evaluating its communication on the one hand, and the interaction with a cloud backend on the other. Bringing together one applied research partner and one automotive supplier from each country combines domain know-how and technological competencies to address the problem, develop new technologies and later enable new transnational services for customers. Transnational dissemination activities and the exchange of young researchers complement the research. To have privacy preserving techniques by design close to deployment in new cars in 2030 requires to start now and bring project results in the specification of the new automotive architectures in 2023-2024, which coincides with the earliest end of the project.

The scouring process represents a significant contributing factor in the destabilising and destruction of civil structures (bridges, earth embankments and buildings) during major flood events, yet our understanding of the mechanisms involved remains highly empirical. The main objective of the SSHEAR project is to improve understanding of this scouring process through the use of innovative observation tools and physical and numerical hydraulic modelling, from laboratory to full-scale, for the purpose of optimising methods specific to diagnostics, advanced warning and general management procedures. This project is intended to create the conditions necessary for an expert opinion to emerge that compensates for, at a national level, a generally acknowledged lack of knowledge. In the case of the French railway network, a comprehensive inventory has been drawn up of infrastructure crossing or located adjacent to waterways. For over 30 years, improvements have continuously been introduced relative to monitoring policy and both the preventive and corrective maintenance of rail and road structures. The practical principles of such monitoring programmes are organised into different actions: periodic and detailed inspections of structures, risk analyses and diagnostics, enhanced surveillance based on the implementation of in situ instrumentation and/or investigation (including bathymetry surveys). However, despite these efforts, a sensitivity classification of structures to the problem of scouring has not been adequately addressed. To overcome this reliance on empiricism, while building general knowledge (especially at the national level) and proposing optimised methods aimed at diagnostics, advanced warning procedures and infrastructure management, SSHEAR sets forth a multi-scale and multi-scientific approach based on: - physical processes of flow and erosion in the vicinity of structures (e.g. bridges, dams, embankments, quay walls), - three laboratory experiments featuring multi-scale observation, - an innovative numerical approach built around a two-phase model, - observations and field recordings of actual structures subjected to hydro-sedimentary forcing imposed due to environmental or anthropogenic actions. The SSHEAR consortium comprises six Partners, whose complementarity offers a major asset to the project, namely: a specialist in soils and fluid mechanics with extensive field practice (Partner 1: Ifsttar); geotechnical and hydraulic engineers together with sedimentologists (Partner 2: Cerema); physicists and engineers focusing on complex systems (Partner 3: FAST); infrastructure management companies (Partner 4: Cofiroute, and Partner 5: SNCF); and a technological research institute, or IRT (Partner 6: Railenium). This combined set of diverse skills makes it possible to conduct a wide range of research on the scouring process and its consequences: - from feedback and measurements in the field to a multi-scale investigation of phenomena - from experimentation to numerical modelling - from fundamental science aspects to engineering aspects - from new knowledge acquisition to practical solutions implemented by end-users.