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of 517
pro vyhledávání: '"Papadopoulos, Symeon"'
Autor:
Karageorgiou, Dimitrios, Bammey, Quentin, Porcellini, Valentin, Goupil, Bertrand, Teyssou, Denis, Papadopoulos, Symeon
Synthetic images disseminated online significantly differ from those used during the training and evaluation of the state-of-the-art detectors. In this work, we analyze the performance of synthetic image detectors as deceptive synthetic images evolve
Externí odkaz:
http://arxiv.org/abs/2408.11541
Computer vision (CV) datasets often exhibit biases that are perpetuated by deep learning models. While recent efforts aim to mitigate these biases and foster fair representations, they fail in complex real-world scenarios. In particular, existing met
Externí odkaz:
http://arxiv.org/abs/2408.11439
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