no code implementations • 26 Jul 2023 • Ryota Iijima, Miki Tanaka, Sayaka Shiota, Hitoshi Kiya
In previous studies, it was confirmed that the vision transformer (ViT) is more robust against the property of adversarial transferability than convolutional neural network (CNN) models such as ConvMixer, and moreover encrypted ViT is more robust than ViT without any encryption.
no code implementations • 19 Sep 2022 • Ryota Iijima, Miki Tanaka, Isao Echizen, Hitoshi Kiya
Deep neural networks (DNNs) are well known to be vulnerable to adversarial examples (AEs).
no code implementations • 7 Sep 2022 • Miki Tanaka, Isao Echizen, Hitoshi Kiya
Deep neural networks (DNNs) are well known to be vulnerable to adversarial examples (AEs).
no code implementations • 10 Aug 2022 • Shoko Niwa, Miki Tanaka, Hitoshi Kiya
In addition, videos are temporally operated such as the insertion of new frames and the permutation of frames, of which operations are difficult to be detected by using conventional methods.
no code implementations • 4 Aug 2021 • Miki Tanaka, Sayaka Shiota, Hitoshi Kiya
In addition, an ensemble of the proposed detector with emphasized spectrums and a conventional detector is proposed to improve the performance of these methods.
no code implementations • 2 Feb 2021 • Miki Tanaka, 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.
no code implementations • 1 Dec 2020 • Takayuki Osakabe, Miki Tanaka, Yuma Kinoshita, Hitoshi Kiya
In this paper, we propose a novel CycleGAN without checkerboard artifacts for counter-forensics of fake-image detection.