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.