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pro vyhledávání: '"Tanaka, Miki"'
Deep neural networks (DNNs) are well known to be vulnerable to adversarial examples (AEs). In addition, AEs have adversarial transferability, which means AEs generated for a source model can fool another black-box model (target model) with a non-triv
Externí odkaz:
http://arxiv.org/abs/2307.13985
Deep neural networks (DNNs) are well known to be vulnerable to adversarial examples (AEs). In addition, AEs have adversarial transferability, which means AEs generated for a source model can fool another black-box model (target model) with a non-triv
Externí odkaz:
http://arxiv.org/abs/2209.08724
Deep neural networks (DNNs) are well known to be vulnerable to adversarial examples (AEs). In addition, AEs have adversarial transferability, namely, AEs generated for a source model fool other (target) models. In this paper, we investigate the trans
Externí odkaz:
http://arxiv.org/abs/2209.02997
SNS providers are known to carry out the recompression and resizing of uploaded videos/images, but most conventional methods for detecting tampered videos/images are not robust enough against such operations. In addition, videos are temporally operat
Externí odkaz:
http://arxiv.org/abs/2208.05198
We propose a novel universal detector for detecting images generated by using CNNs. In this paper, properties of checkerboard artifacts in CNN-generated images are considered, and the spectrum of images is enhanced in accordance with the properties.
Externí odkaz:
http://arxiv.org/abs/2108.01892
Autor:
Tanaka, Miki, Kiya, Hitoshi
In this paper, we investigate whether robust hashing has a possibility to robustly detect fake-images even when multiple manipulation techniques such as JPEG compression are applied to images for the first time. In an experiment, the proposed fake de
Externí odkaz:
http://arxiv.org/abs/2102.01313
In this paper, we propose a novel CycleGAN without checkerboard artifacts for counter-forensics of fake-image detection. Recent rapid advances in image manipulation tools and deep image synthesis techniques, such as Generative Adversarial Networks (G
Externí odkaz:
http://arxiv.org/abs/2012.00287
Autor:
Tanaka, Miki
This thesis provides an in-depth study of the properties of pseudo-distributive laws, and as one of its applications, a unified framework to model substitution and variable binding for various different types of contexts; in particular, the construct
Externí odkaz:
http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.662722
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