We are developing technologies and services for understanding, searching and verifying media content
We develop methods and tools for bringing Trustworthy AI and advanced media analytics into new application settings and contexts.
We provide tools for image forensics, Exif metadata analysis, synthetic image detection, visual location estimation and video deepfake detection.
We have solutions for detecting Not Safe For Work (NSFW) and disturbing images and videos.
We provide methods and services for reverse video search using audio-visual similarity on large collections of videos.
We have integrated a number of advanced computer vision and media retrieval methods into a complete web application that can serve diverse media asset management needs.
We offer methods and expertise on measuring and addressing bias and discriminatory behaviour in computer vision models.
We offer tools and expertise on analysis and visualization of online social media connections, conversations and communities.
We offer support for integrating cutting edge ΑΙ models into web services and end user applications.
We have a long successful track record of research and innovation project coordination, and can provide consulting and research project management services.
The Media Verification team has extensive experience and expertise in the area of online disinformation with an emphasis on multimedia-mediated disinformation.
This repository contains the Tensorflow implementation of the paper Near-Duplicate Video Retrieval with Deep Metric Learning.
This is an integrated framework for image forensic analysis.
A framework for “learning” how to classify social content as truthful/reliable or not. Features are extracted from the tweet text (Tweet-based features TF) and the user who published it (User-based features UB). A two level classification model is trained.
This repository contains the implementation of algorithms that estimate the geographic location of multimedia items based on their textual content. The approach is described in the paper Geotagging Text Content With Language Models and Feature Mining.
Intermediate CNN Features
This repository contains the implementation of the feature extraction process described in Near-Duplicate Video Retrieval by Aggregating Intermediate CNN Layers. Given an input video, one frame per second is sampled and its visual descriptor is extracted from the activations of the intermediate convolution layers of a pre-trained Convolutional Neural Network. Then, the Maximum Activation of Convolutions (MAC) function is applied on the activation of each layer to generate a compact layer vector. Finally, the layer vector are concatenated to generate a single frame descriptor.
Near-Duplicate Video Retrieval with Deep Metric Learning
This repository contains the Tensorflow implementation of the paper Near-Duplicate Video Retrieval with Deep Metric Learning. It provides code for training and evalutation of a Deep Metric Learning (DML) network on the problem of Near-Duplicate Video Retrieval (NDVR). During training, the DML network is fed with video triplets, generated by a triplet generator. The network is trained based on the triplet loss function.
MedDMO addresses the need of the European Digital Media Observatory (EDMO) to expand its regional coverage in EU countries and create a multinational, multilingual, and cross-sectoral hub focused on fact-checking, research, and education to counter disinformation in Malta, Greece and Cyprus. Several multimedia analysis tools are made available to assist fact-checkers and researchers in their work against disinformation. Media literacy activities and the organisation of awareness campaigns will be central to MedDMO, trying to build resilience and adaptability against disinformation among citizens and media in the Mediterranean region.
Dec 2022 – May 2025Read more
MAMMOth aims at developing an innovative fairness-aware AI-data driven foundation that provides the necessary tools and techniques for the discovery and mitigation of multi-discrimination and ensures the accountability of AI-systems with respect to multiple protected attributes and for traditional tabular data and more complex network and visual data. The outcomes of research in MAMMOth will be made available both as standalone open-source components and integrated into an open source toolkit “MAMMOth toolkit”. The project also comprises active interaction with multiple communities of vulnerable and/or underrepresented groups in AI research, implementing a co-creation strategy to ensure that genuine user needs and pains are at the center of the research agenda.
Nov 2022 - Oct 2025Read more
vera.ai seeks to build trustworthy AI solutions against advanced disinformation techniques, co-created with and for media professionals and set the foundation for future research in the area of AI against disinformation. Key novel characteristics of the AI models will be fairness, transparency (including explainability), robustness to new data, and continuous adaptation to new disinformation techniques.
Sep 2022 - Aug 2025Read more
AI4Media aims to address the challenges, risks, and opportunities that the wide use of AI brings to media, society, and politics. The project aspires to become a centre of excellence and a wide network of researchers across Europe and beyond, with a focus on delivering the next generation of core AI advances to serve the key sector of Media.
Sep 2020 - Aug 2024Read more
In this post, we explain the basics behind our paper “VERITE: a robust benchmark for multimodal misinformation detection accounting for unimodal bias”, by Stefanos-Iordanis Papadopoulos, Christos Koutlis, Symeon Papadopoulos and Panagiotis C. Petrantonakis, which has been published in the International Journal of Multimedia Information Retrieval (IJMIR). Given the rampant spread of misinformation, the role of fact-checkers becomes increasingly important and their task more challenging given the sheer volume of content generated and shared daily across social media platforms.
Large Language Models (LLMs) are massive neural network architectures trained on vast amounts of data while being tasked to classify or generate text. These models have acquired a deep understanding of the intrinsic language structure as well as the world itself by analyzing web-scraped documents word after word, sentence after sentence for a long machine-time.
In the era of smartphones and social media, where everyone is able to capture the moment and share it online in seconds, knowing the origin of an image could make the difference between early debunking of a fabricated story and the continued spread of a false message. In this blog post, we will take a look at the task of identifying the source of an image, a key problem in the field of image provenance analysis.