18 citations · 43 across the 5 of their papers we have counts for
5 papers
Black-box Adversarial Attacks on Monocular Depth Estimation Using Evolutionary Multi-objective Optimization
Renya Daimo, Satoshi Ono, Takahiro Suzuki
This paper proposes an adversarial attack method to deep neural networks (DNNs) for monocular depth estimation, i.e., estimating the depth from a single image. Single image depth e…
Adjust-free adversarial example generation in speech recognition using evolutionary multi-objective optimization under black-box condition
Shoma Ishida, Satoshi Ono
This paper proposes a black-box adversarial attack method to automatic speech recognition systems. Some studies have attempted to attack neural networks for speech recognition; how…
Depth estimation from 4D light field videos
Takahiro Kinoshita, Satoshi Ono
Depth (disparity) estimation from 4D Light Field (LF) images has been a research topic for the last couple of years. Most studies have focused on depth estimation from static 4D LF…
Adversarial Example Generation using Evolutionary Multi-objective Optimization
Takahiro Suzuki, Shingo Takeshita, Satoshi Ono
This paper proposes Evolutionary Multi-objective Optimization (EMO)-based Adversarial Example (AE) design method that performs under black-box setting. Previous gradient-based meth…
Temporal shape super-resolution by intra-frame motion encoding using high-fps structured light
Yuki Shiba, Satoshi Ono, Ryo Furukawa +2
One of the solutions of depth imaging of moving scene is to project a static pattern on the object and use just a single image for reconstruction. However, if the motion of the obj…