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cs.CV2026

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…

cs.CV2025

"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…

cs.CV2025

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…

cs.CV2025

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…

cs.CV2024

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…