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. 2023). Motivated by this success, we hypothesize that further performance gains are possible by leveraging representations from intermediate layers, which carry low-level visual information, in addition to representations from the final layer that primarily carry high-level semantic information.

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. To combat this, many fact-checking organizations, including Snopes, PolitiFact as well as Reuters and AFP fact-checks, have emerged, dedicated to verifying claims in news articles and social media posts. Nevertheless, the manual process of fact-checking is time-consuming and can’t always keep pace with the rapid spread of mis- and disinformation. This is where the field of Automated Fact-Checking (AFC) comes in. In recent years, researchers have been trying to leverage advances in deep learning, large language models, computer vision, and multimodal learning to develop tools to assist the work of professional fact-checkers. AFC systems aim to automate key parts of the fact-checking process, such as detecting check-worthy claims, retrieving relevant evidence from the web, and cross-examining them against the claim (Guo, et al., 2022). Since fact-checking rarely relies solely on examining internal contradictions in claims, AFC systems often require the retrieval of external information from the web, knowledge databases, or performing inverse image searches to support or refute a claim, as shown below.

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. This is where our new tool, FaceX, comes into play.

As AI systems become more ingrained in everyday life, it is important to address fairness concerns. For example, machine learning models are known to pick up and exacerbate biases found in their training data. So far, various attempts have been made to quantify and mitigate unfair AI biases, for example with algorithmic frameworks like AIF360. However, these attempts tend to work on a case-by-case basis, with tackled measures of bias or fairness being restricted to simplistic settings, such as binary sensitive attributes (e.g., men vs women, whites vs blacks). For this reason, in the context of the Horizon Europe MAMMOth project we developed a Python library called FairBench to help system creators assess bias and fairness in complex scenarios.

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. Many current face image datasets primarily focus on representativeness in terms of demographic factors such as age, gender, and skin tone, overlooking other crucial facial attributes like hairstyle and accessories. This narrow focus limits the diversity of the data and consequently the robustness of AI systems trained on them.

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. Studies reveal that multimedia content, with its visual appeal, tends to capture attention more effectively and enjoys broader dissemination compared to plain text (Li & Xie, 2019) and the inclusion of images can notably amplify the persuasiveness of false statements (Newman et al., 2012). It is for that reason that Multimodal Misinformation (MM) is very concerning. MM involves false or misleading information disseminated through diverse “modalities” of communication, including text, images, audio, and video. Scenarios often unfold where an image is removed from its authentic context, or its accompanying caption distorts critical elements, such as the event, date, location, or the identity of the depicted person. For instance, in the above figure (a) we observe an image where the grounds of a musical festival are covered in garbage, accompanied by the claim that it was taken in June 2022 “after Greta Thunberg’s environmentalist speech”. However, the image was removed from its authentic context, since it was actually taken in 2015, not 2022.