In this post we present our dual CNN methodology, that reduces inference energy requirements of CNNs in image classification tasks on low-resource devices. Further details about our approach and associated experiments are included in our paper Reducing inference energy consumption using dual complementary CNNs, which has been accepted for publication in the journal of Future Generation Computer Systems.

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.

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.

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 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. Also, we will present our findings on how most of the big digital platforms modify the popular JPEG standard and how we use these traces for identifying the source of images that circulate online without any prior knowledge or assumption.

Last year, we had the opportunity to conduct a desk research study in an effort to map the landscape of online disinformation in Europe. Our research was guided by three research questions: What are the measures and journalistic authorities in Europe that supervise and monitor the ethical application of journalism? Are there patterns of false information spreading as a means to serve the agenda and interests of certain political groups? Are there certain political groups that benefit as a result of given disinformation narratives? Are there specific and tangible policies that have been proposed by European and international organisations that could help tackle online disinformation? To make the study of the first two questions more manageable, we focused on five European countries: France, Germany, Greece, Italy and Spain, and on three topics that are commonly associated with disinformation: COVID-19, immigration, and climate change. The resulting report is available here and we believe it could be valuable for informing a number of stakeholders, including professionals in the media industry and policy makers. In the following, we try to present the key insights that we managed to draw from this report.