In this post we present the essential parts of our method RINE, described in the paper Leveraging Representations from Intermediate Encoder-Blocks for Synthetic Image Detection, which has been accepted by the European Conference on Computer Vision (ECCV 2024). Motivation Recent research on Synthetic Image Detection (SID) has led to strong evidence on the advantages of representations extracted by foundation models, exhibiting exceptional generalization capabilities on GAN and Diffusion model generated data (Ojha et al.

In this post, we explain the basics behind our paper Credible, Unreliable or Leaked?: Evidence verification for enhanced automated fact-checking by Zacharias Chrysidis, Stefanos-Iordanis Papadopoulos, Symeon Papadopoulos and Panagiotis C. Petrantonakis, which has been presented at ICMR’s 3rd ACM International Workshop on Multimedia AI against Disinformation. Automated Fact-Checking (AFC) The Information Age, especially after the explosion of online platforms and social media, has led to a surge in new forms of mis- and disinformation, making it increasingly difficult for people to trust what they see and read online.

The problem Generative AI is making it easier to create incredibly realistic synthetic images. Detecting these fake images is challenging, and several methods have been developed to tackle this issue. However, there’s often a big difference between how well these methods work in experiments and how they perform in real life.

In today’s digital age, professionals such as journalists and human rights investigators often encounter distressing and graphic imagery as part of their work. The emotional toll of viewing such content can be profound, leading to secondary trauma and affecting mental well-being. Traditional methods like blurring have been used to shield viewers from explicit details, but they often compromise image interpretability.

In today’s world, AI systems are integral to many applications, from unlocking smartphones to recognizing emotions. While these advancements bring numerous benefits, they also raise ethical and societal concerns, particularly regarding AI bias. Facial analysis technologies, in particular, have been scrutinized for their potential biases, which can disproportionately affect certain groups of people.

Artificial Intelligence (AI) systems today are incredibly advanced, leveraging vast amounts of data to perform complex tasks with high accuracy. These systems require huge datasets to train effectively. The quality and quantity of data significantly impact their performance, enabling them to recognize patterns, make predictions, and improve decision-making processes. As AI continues to evolve, the demand for large, diverse, and high-quality datasets increases especially, for image-based systems dealing with demographic attribute prediction that often face considerable bias and discrimination issues.

In the context of the Horizon Europe MAMMOth project, we developed the “AI Fairness Definition Guide” to help those creating AI (such as researchers, developers, and product owners) understand how to define fairness in the social context of their created systems by working with stakeholders and experts from other disciplines. The guide presents a workflow for gathering fairness concerns of affected stakeholders and using them to derive corresponding formalisms and practices under a combined computer, social, and legal science viewpoint.

This post provides an overview of the paper “FRCSyn-onGoing: Benchmarking and comprehensive evaluation of real and synthetic data to improve face recognition systems” , which has been published in the Information Fusioninformation-fusion journal. This has been a collaborative effort led by the organizers of the FRCSyn-onGoing challenge and several of the participating teams, such as ours.

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.

In this post, we explain the basics behind our paper “DnS: Distill-and-Select for Efficient and Accurate Video Indexing and Retrieval”, which has been accepted for publication in the International Journal of Computer Vision (IJCV). Following our prior work in ViSiL, we realised that high-performing video retrieval systems require a significant amount of compute and storage resources.