A Blockchain-based G-code Protection Approach for Cyber-Physical Security in Additive Manufacturing

2020 ◽  
pp. 1-29
Author(s):  
Zhangyue Shi ◽  
Chen Kan ◽  
Wenmeng Tian ◽  
Chenang Liu
Keyword(s):  
Additive Manufacturing ◽  
Data Storage ◽  
Real World ◽  
Physical Security ◽  
Physical Attack ◽  
Real World Applications ◽  
The Face ◽  
G Code ◽  
Code Security ◽  
Intellectual Property Theft

Abstract As an emerging technology, additive manufacturing (AM) is able to fabricate products with complex geometries using various materials. In particular, cyber-enabled AM systems have recently become widely applied in many real-world applications. It significantly improves the flexibility and productivity of AM but poses the system under high risks of cyber-physical attacks. For example, cyber-physical attack could maliciously tamper the product design and process parameters, which, in turn, leads to significant alteration of the desired properties in AM products. Therefore, there is an urgent need in incorporating advanced technologies to improve the cyber-physical security for the cyber-enabled AM systems. In this study, two common types of cyber-physical attacks regarding the G-code security were investigated, namely, unintended design modifications and intellectual property theft. To effectively secure the G-code against these two attacks, a new methodology is developed in this study, which consists of a novel blockchain-based data storage approach and an effective asymmetry encryption technique. The proposed method was also applied to a real-world AM case for ensuring the cyber-physical security of the face shield fabrication, which is critical during the COVID-19 pandemic. Based on the proposed methodology, malicious tampering can be accurately detected in a timely manner and meanwhile the risk of unauthorized access of the G-code file will be greatly eliminated as well.

3D Technology – Additive Manufacturing Applications and Prospects (Vol. 24, No. 9, Full Issue)

2020 ◽  
Vol 24 (09) ◽  
Keyword(s):  
Additive Manufacturing ◽  
3D Printing ◽  
Environmental Impact ◽  
Real World ◽  
Vaccine Development ◽  
Health Screening ◽  
Wide Range ◽  
The World ◽  
Real World Applications ◽  
Manufacturing Applications

For the month of September 2020, APBN dives into the world of 3D printing and its wide range of real-world applications. Keeping our focus on the topic of the year, the COVID-19 pandemic, we explore the environmental impact of the global outbreak as well as gain insight to the top 5 vaccine platforms used in vaccine development. Discover more about technological advancements and how it is assisting innovation in geriatric health screening.

Face Counting and Mask Detection using Deep Learning

2020 ◽  
Vol 11 (12) ◽  
pp. 709-714
Author(s):  
Janani Prabu ◽  
Sai Saranesh ◽  
Dr.S. Ajitha
Keyword(s):  
Deep Learning ◽  
Face Recognition ◽  
Strong Solution ◽  
Real World ◽  
Human Communication ◽  
Human Ability ◽  
Real World Applications ◽  
The Face

Face is one among the foremost important human's biometrics which is used frequently in every day human communication and due to some of its unique characteristics plays a major role in conveying identity and emotion. So far numerous methods have been proposed for face recognition, but it's still remained very challenging in real world applications and up to date; there is no technique which equals human ability to recognize faces despite many variations in appearance that the face can have in a scene and provides a strong solution to all situations.

scholarly journals Generative Facial Prior for Large-Factor Blind Face Super-Resolution

2021 ◽  
Vol 2078 (1) ◽  
pp. 012045
Author(s):  
Xiaomeng Guo ◽  
Li Yi ◽  
Hang Zou ◽  
Yining Gao
Keyword(s):  
Real World ◽  
Prior Information ◽  
Super Resolution ◽  
Feature Transformation ◽  
Magnification Factor ◽  
High Quality ◽  
Large Factor ◽  
Real World Applications ◽  
The Face ◽  
The Rich

Abstract Most existing face super-resolution (SR) methods are developed based on an assumption that the degradation is fixed and known (e.g., bicubic down sampling). However, these methods suffer a severe performance drop in various unknown degradations in real-world applications. Previous methods usually rely on facial priors, such as facial geometry prior or reference prior, to restore realistic face details. Nevertheless, low-quality inputs cannot provide accurate geometric priors while high-quality references are often unavailable, which limits the use of face super-resolution in real-world scenes. In this work, we propose GPLSR which used the rich priors encapsulated in the pre-trained face GAN network to perform blind face super-resolution. This generative facial priori is introduced into the face super-resolution process through channel squeeze-and-excitation spatial feature transformation layer (SE-SFT), which makes our method achieve a good balance between realness and fidelity. Moreover, GPLSR can restores facial details with single forward pass because of powerful generative facial prior information. Extensive experiment shows that when the magnification factor is 16, this method achieves better performance than existing techniques in both synthetic and real datasets.

scholarly journals Model Fast Tansfer Learning pada Jaringan Syaraf Tiruan Konvolusional untuk Klasifikasi Gender Berdasarkan Citra Wajah

2019 ◽  
Vol 18 (2) ◽  
pp. 211-221
Author(s):  
Bambang Krismono Triwijoyo
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Transfer Learning ◽  
Real World ◽  
Face Image ◽  
Gender Classification ◽  
Learning Method ◽  
Image Transfer ◽  
Real World Applications ◽  
The Face

The face is a challenging object to be recognized and analyzed automatically by a computer in many interesting applications such as facial gender classification. The large visual variations of faces, such as occlusions, pose changes, and extreme lightings, impose great challenge for these tasks in real world applications. This paper explained the fast transfer learning representations through use of convolutional neural network (CNN) model for gender classification from face image. Transfer learning aims to provide a framework to utilize previously-acquired knowledge to solve new but similar problems much more quickly and effectively. The experimental results showed that the transfer learning method have faster and higher accuracy than CNN network without transfer learning.

Vignettes

2018 ◽  
Author(s):  
Marc J. Stern
Keyword(s):  
Real World ◽  
Environmental Sustainability ◽  
National Park ◽  
Public Involvement ◽  
Energy Savings ◽  
Rapid Development ◽  
Environmental Problems ◽  
Persuasive Communications ◽  
For Profit ◽  
The Face

Chapter 9 contains five vignettes, each based on real world cases. In each, a character is faced with a problem and uses multiple theories within the book to help him or her develop and execute a plan of action. The vignettes provide concrete examples of how to apply the theories in the book to solving environmental problems and working toward environmental sustainability in a variety of contexts, including managing visitors in a national park, developing persuasive communications, designing more collaborative public involvement processes, starting up an energy savings program within a for-profit corporation, and promoting conservation in the face of rapid development.

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