Zobrazeno 1 - 10
of 51
pro vyhledávání: '"Karjauv A"'
Autor:
Karjauv, Aray, Albayrak, Sahin
Convolutional Neural Networks (CNNs) are known for their ability to learn hierarchical structures, naturally developing detectors for objects, and semantic concepts within their deeper layers. Activation maps (AMs) reveal these saliency regions, whic
Externí odkaz:
http://arxiv.org/abs/2407.06059
Autor:
Kahatapitiya, Kumara, Karjauv, Adil, Abati, Davide, Porikli, Fatih, Asano, Yuki M., Habibian, Amirhossein
Diffusion-based video editing have reached impressive quality and can transform either the global style, local structure, and attributes of given video inputs, following textual edit prompts. However, such solutions typically incur heavy memory and c
Externí odkaz:
http://arxiv.org/abs/2401.05735
Autor:
Homeyer, André, Geißler, Christian, Schwen, Lars Ole, Zakrzewski, Falk, Evans, Theodore, Strohmenger, Klaus, Westphal, Max, Bülow, Roman David, Kargl, Michaela, Karjauv, Aray, Munné-Bertran, Isidre, Retzlaff, Carl Orge, Romero-López, Adrià, Sołtysiński, Tomasz, Plass, Markus, Carvalho, Rita, Steinbach, Peter, Lan, Yu-Chia, Bouteldja, Nassim, Haber, David, Rojas-Carulla, Mateo, Sadr, Alireza Vafaei, Kraft, Matthias, Krüger, Daniel, Fick, Rutger, Lang, Tobias, Boor, Peter, Müller, Heimo, Hufnagl, Peter, Zerbe, Norman
Publikováno v:
Mod Pathol (2022)
Artificial intelligence (AI) solutions that automatically extract information from digital histology images have shown great promise for improving pathological diagnosis. Prior to routine use, it is important to evaluate their predictive performance
Externí odkaz:
http://arxiv.org/abs/2204.14226
Existing works have identified the limitation of top-$1$ attack success rate (ASR) as a metric to evaluate the attack strength but exclusively investigated it in the white-box setting, while our work extends it to a more practical black-box setting:
Externí odkaz:
http://arxiv.org/abs/2204.00089
Convolutional Neural Networks (CNNs) have become the de facto gold standard in computer vision applications in the past years. Recently, however, new model architectures have been proposed challenging the status quo. The Vision Transformer (ViT) reli
Externí odkaz:
http://arxiv.org/abs/2110.02797
Despite their overwhelming success on a wide range of applications, convolutional neural networks (CNNs) are widely recognized to be vulnerable to adversarial examples. This intriguing phenomenon led to a competition between adversarial attacks and d
Externí odkaz:
http://arxiv.org/abs/2104.03000
Publikováno v:
International Joint Conferences on Artificial Intelligence (IJCAI) 2021, survey track
The intriguing phenomenon of adversarial examples has attracted significant attention in machine learning and what might be more surprising to the community is the existence of universal adversarial perturbations (UAPs), i.e. a single perturbation to
Externí odkaz:
http://arxiv.org/abs/2103.01498
The booming interest in adversarial attacks stems from a misalignment between human vision and a deep neural network (DNN), i.e. a human imperceptible perturbation fools the DNN. Moreover, a single perturbation, often called universal adversarial per
Externí odkaz:
http://arxiv.org/abs/2102.06479
Data hiding is one widely used approach for protecting authentication and ownership. Most multimedia content like images and videos are transmitted or saved in the compressed form. This kind of lossy compression, such as JPEG, can destroy the hidden
Externí odkaz:
http://arxiv.org/abs/2101.00973
Convolutional neural networks (CNNs) have made significant advancement, however, they are widely known to be vulnerable to adversarial attacks. Adversarial training is the most widely used technique for improving adversarial robustness to strong whit
Externí odkaz:
http://arxiv.org/abs/2010.13365