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pro vyhledávání: '"Papadopoulos, Symeon"'
Deepfake technology has rapidly advanced, posing significant threats to information integrity and societal trust. While significant progress has been made in detecting deepfakes, the simultaneous manipulation of audio and visual modalities, sometimes
Externí odkaz:
http://arxiv.org/abs/2411.10193
Deep learning techniques have been successfully applied in Synthetic Aperture Radar (SAR) target recognition in static scenarios relying on predefined datasets. However, in real-world scenarios, models must incrementally learn new information without
Externí odkaz:
http://arxiv.org/abs/2410.05820
Autor:
Huertas-Tato, Javier, Koutlis, Christos, Papadopoulos, Symeon, Camacho, David, Kompatsiaris, Ioannis
Memes are an increasingly prevalent element of online discourse in social networks, especially among young audiences. They carry ideas and messages that range from humorous to hateful, and are widely consumed. Their potentially high impact requires a
Externí odkaz:
http://arxiv.org/abs/2409.05772
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