Zobrazeno 1 - 10
of 444
pro vyhledávání: '"Papadopoulos, Symeon"'
Generative AI technologies produce hyper-realistic imagery that can be used for nefarious purposes such as producing misleading or harmful content, among others. This makes Synthetic Image Detection (SID) an essential tool for defending against AI-ge
Externí odkaz:
http://arxiv.org/abs/2407.15500
Autor:
Papadopoulos, Stefanos-Iordanis, Koutlis, Christos, Papadopoulos, Symeon, Petrantonakis, Panagiotis C.
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 support false narratives. Recent research in evidence-based OOC detection ha
Externí odkaz:
http://arxiv.org/abs/2407.13488
Autor:
Mareen, Hannes, Karageorgiou, Dimitrios, Van Wallendael, Glenn, Lambert, Peter, Papadopoulos, Symeon
Digital image manipulation has become increasingly accessible and realistic with the advent of generative AI technologies. Recent developments allow for text-guided inpainting, making sophisticated image edits possible with minimal effort. This poses
Externí odkaz:
http://arxiv.org/abs/2407.11566
Artificial intelligence systems often address fairness concerns by evaluating and mitigating measures of group discrimination, for example that indicate biases against certain genders or races. However, what constitutes group fairness depends on who
Externí odkaz:
http://arxiv.org/abs/2406.18939
This paper discusses four facets of the Knowledge Distillation (KD) process for Convolutional Neural Networks (CNNs) and Vision Transformer (ViT) architectures, particularly when executed on edge devices with constrained processing capabilities. Firs
Externí odkaz:
http://arxiv.org/abs/2407.12808
Creating fair AI systems is a complex problem that involves the assessment of context-dependent bias concerns. Existing research and programming libraries express specific concerns as measures of bias that they aim to constrain or mitigate. In practi
Externí odkaz:
http://arxiv.org/abs/2405.19022
Synthetically generated images can be used to create media content or to complement datasets for training image analysis models. Several methods have recently been proposed for the synthesis of high-fidelity face images; however, the potential biases
Externí odkaz:
http://arxiv.org/abs/2405.11320
Autor:
Chrysidis, Zacharias, Papadopoulos, Stefanos-Iordanis, Papadopoulos, Symeon, Petrantonakis, Panagiotis C.
Automated fact-checking (AFC) is garnering increasing attention by researchers aiming to help fact-checkers combat the increasing spread of misinformation online. While many existing AFC methods incorporate external information from the Web to help e
Externí odkaz:
http://arxiv.org/abs/2404.18971
Autor:
Tsigos, Konstantinos, Apostolidis, Evlampios, Baxevanakis, Spyridon, Papadopoulos, Symeon, Mezaris, Vasileios
In this paper we propose a new framework for evaluating the performance of explanation methods on the decisions of a deepfake detector. This framework assesses the ability of an explanation method to spot the regions of a fake image with the biggest
Externí odkaz:
http://arxiv.org/abs/2404.18649
Autor:
Schinas, Manos, Papadopoulos, Symeon
The generative AI technology offers an increasing variety of tools for generating entirely synthetic images that are increasingly indistinguishable from real ones. Unlike methods that alter portions of an image, the creation of completely synthetic i
Externí odkaz:
http://arxiv.org/abs/2404.18552