Characterizing Adversarial Examples Based on Spatial Consistency Information for Semantic Segmentation

Fast 3D reconstruction with semantic information on road scenes is of great requirements for autonomous navigation. It involves issues of geometry and appearance in the field of computer vision. In this work, we propose a method of fast 3D semantic mapping based on the monocular vision. At present, due to the inexpensive price and easy installation, monocular cameras are widely equipped on recent vehicles for the advanced driver assistance and it is possible to acquire semantic information and 3D map. The monocular visual sequence is used to estimate the camera pose, calculate the depth, predict the semantic segmentation, and finally realize the 3D semantic mapping by combination of the techniques of localization, mapping and scene parsing. Our method recovers the 3D semantic mapping by incrementally transferring 2D semantic information to 3D point cloud. And a global optimization is explored to improve the accuracy of the semantic mapping in light of the spatial consistency. In our framework, there is no need to make semantic inference on each frame of the sequence, since the mesh data with semantic information is corresponding to sparse reference frames. It saves amounts of the computational cost and allows our mapping system to perform online. We evaluate the system on naturalistic road scenes, e.g., KITTI and observe a significant speed-up in the inference stage by labeling on the mesh.

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Imperceptible Adversarial Attacks against Traffic Scene Recognition

10.21203/rs.3.rs-652216/v1 ◽

2021 ◽

Author(s):

Yinghui Zhu ◽

Yuzhen Jiang

Keyword(s):

Semantic Segmentation ◽

Absolute Error ◽

Scene Recognition ◽

Lane Detection ◽

Traffic Sign ◽

Adversarial Examples ◽

The Mean ◽

Street Scene ◽

Human Eyes ◽

To Receive

Abstract Adversarial examples have begun to receive widespread attention owning to their potential destructions to the most popular DNNs. They are crafted from original images by embedding well calculated perturbations. In some cases the perturbations are so slight that neither human eyes nor monitoring systems can notice easily and such imperceptibility makes them have greater concealment and damage. For the sake of investigating the invisible dangers in the applications of traffic DNNs, we focus on imperceptible adversarial attacks on different traffic vision tasks, including traffic sign classification, lane detection and street scene recognition. We propose an universal logits map-based attack architecture against image semantic segmentation, and design two targeted attack approaches on it. All the attack algorithms generate the micro-noise adversarial examples by the iterative method of gradient descent optimization. All of them can achieve 100% attack rate but with very low distortion, among which, the mean MAE (Mean Absolute Error) of perturbation noise based on traffic sign classifier attack is as low as 0.562, and the other two algorithms based on semantic segmentation are only 1.574 and 1.503. We believe that our research on imperceptible adversarial attacks can be of substantial reference to the security of DNNs applications.

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Face Completion Using Semantic Segmentation and Geometric Features

International Review of Automatic Control (IREACO) ◽

10.15866/ireaco.v11i6.15738 ◽

2018 ◽

Vol 11 (6) ◽

pp. 304

Author(s):

Javier Pinzon-Arenas ◽

Robinson Jimenez-Moreno ◽

Ruben Hernandez-Beleno

Keyword(s):

Semantic Segmentation ◽

Geometric Features

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Multi-model Integrated Weakly Supervised Semantic Segmentation Method

Journal of Computer-Aided Design & Computer Graphics ◽

10.3724/sp.j.1089.2019.17379 ◽

2019 ◽

Vol 31 (5) ◽

pp. 800

Author(s):

Changzhen Xiong ◽

Hui Zhi

Keyword(s):

Semantic Segmentation ◽

Segmentation Method ◽

Weakly Supervised

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Development of environment design support mixed reality system capable of environment estimation using deep learning

Impact ◽

10.21820/23987073.2020.2.9 ◽

2020 ◽

Vol 2020 (2) ◽

pp. 9-11

Author(s):

Tomohiro Fukuda

Keyword(s):

Deep Learning ◽

Real Time ◽

Computer Games ◽

Construction Projects ◽

Mixed Reality ◽

Semantic Segmentation ◽

Environment Design ◽

Aviation Training ◽

Architecture And Design ◽

World Environment

Mixed reality (MR) is rapidly becoming a vital tool, not just in gaming, but also in education, medicine, construction and environmental management. The term refers to systems in which computer-generated content is superimposed over objects in a real-world environment across one or more sensory modalities. Although most of us have heard of the use of MR in computer games, it also has applications in military and aviation training, as well as tourism, healthcare and more. In addition, it has the potential for use in architecture and design, where buildings can be superimposed in existing locations to render 3D generations of plans. However, one major challenge that remains in MR development is the issue of real-time occlusion. This refers to hiding 3D virtual objects behind real articles. Dr Tomohiro Fukuda, who is based at the Division of Sustainable Energy and Environmental Engineering, Graduate School of Engineering at Osaka University in Japan, is an expert in this field. Researchers, led by Dr Tomohiro Fukuda, are tackling the issue of occlusion in MR. They are currently developing a MR system that realises real-time occlusion by harnessing deep learning to achieve an outdoor landscape design simulation using a semantic segmentation technique. This methodology can be used to automatically estimate the visual environment prior to and after construction projects.

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