13 citations · 29 across the 11 of their papers we have counts for
11 papers
Evidence Triangulation for Multimodal Fact-Checking in the Wild
Stefanos-Iordanis Papadopoulos, Zacharias Chrysidis, Christos Koutlis +2
The proliferation of multimedia content on social platforms has fueled multimodal misinformation, where images are used to reinforce false claims. Consequently, Multimodal Fact-Che…
"Humor, Art, or Misinformation?": A Multimodal Dataset for Intent-Aware Synthetic Image Detection
Anastasios Skoularikis, Stefanos-Iordanis Papadopoulos, Symeon Papadopoulos +1
Recent advances in multimodal AI have enabled progress in detecting synthetic and out-of-context content. However, existing efforts largely overlook the intent behind AI-generated…
Composite Data Augmentations for Synthetic Image Detection Against Real-World Perturbations
Efthymia Amarantidou, Christos Koutlis, Symeon Papadopoulos +1
The advent of accessible Generative AI tools enables anyone to create and spread synthetic images on social media, often with the intention to mislead, thus posing a significant th…
Latent Reconstruction from Generated Data for Multimodal Misinformation Detection
Stefanos-Iordanis Papadopoulos, Christos Koutlis, Symeon Papadopoulos +1
Multimodal misinformation, such as miscaptioned images, where captions misrepresent an image's origin, context, or meaning, poses a growing challenge in the digital age. Due to the…
SAGI: Semantically Aligned and Uncertainty Guided AI Image Inpainting
Paschalis Giakoumoglou, Dimitrios Karageorgiou, Symeon Papadopoulos +1
Recent advancements in generative AI have made text-guided image inpainting - adding, removing, or altering image regions using textual prompts - widely accessible. However, genera…
Similarity over Factuality: Are we making progress on multimodal out-of-context misinformation detection?
Stefanos-Iordanis Papadopoulos, Christos Koutlis, Symeon Papadopoulos +1
Out-of-context (OOC) misinformation poses a significant challenge in multimodal fact-checking, where images are paired with texts that misrepresent their original context to suppor…
Credible, Unreliable or Leaked?: Evidence Verification for Enhanced Automated Fact-checking
Zacharias Chrysidis, Stefanos-Iordanis Papadopoulos, Symeon Papadopoulos +1
Automated fact-checking (AFC) is garnering increasing attention by researchers aiming to help fact-checkers combat the increasing spread of misinformation online. While many existi…
RED-DOT: Multimodal Fact-checking via Relevant Evidence Detection
Stefanos-Iordanis Papadopoulos, Christos Koutlis, Symeon Papadopoulos +1
Online misinformation is often multimodal in nature, i.e., it is caused by misleading associations between texts and accompanying images. To support the fact-checking process, rese…
VERITE: A Robust Benchmark for Multimodal Misinformation Detection Accounting for Unimodal Bias
Stefanos-Iordanis Papadopoulos, Christos Koutlis, Symeon Papadopoulos +1
Multimedia content has become ubiquitous on social media platforms, leading to the rise of multimodal misinformation (MM) and the urgent need for effective strategies to detect and…
Synthetic Misinformers: Generating and Combating Multimodal Misinformation
Stefanos-Iordanis Papadopoulos, Christos Koutlis, Symeon Papadopoulos +1
With the expansion of social media and the increasing dissemination of multimedia content, the spread of misinformation has become a major concern. This necessitates effective stra…
Removing Noise from Extracellular Neural Recordings Using Fully Convolutional Denoising Autoencoders
Christodoulos Kechris, Alexandros Delitzas, Vasileios Matsoukas +1
Extracellular recordings are severely contaminated by a considerable amount of noise sources, rendering the denoising process an extremely challenging task that should be tackled f…