Four short links: 14 October 2019
Detecting Manipulated Face Images, Deep Learning Cheat Sheets, Chinese Cybersecurity, and Streaming Dataflow
- FaceForensics++: Learning to Detect Manipulated Facial Images — This paper examines the realism of state-of-the-art image manipulations, and how difficult it is to detect them, either automatically or by humans. To standardize the evaluation of detection methods, we propose an automated benchmark for facial manipulation detection. (GitHub)
- CS 230 — My twin brother Afshine and I created this set of illustrated deep learning cheat sheets covering the content of the CS 230 class, which I TA-ed in Winter 2019 at Stanford. They can (hopefully!) be useful to all future students of this course as well as to anyone else interested in deep learning.
- China’s New Cybersecurity Program: NO Place to Hide — This system will apply to foreign owned companies in China on the same basis as to all Chinese persons, entities, or individuals. No information contained on any server located within China will be exempted from this full coverage program. No communication from or to China will be exempted. There will be no secrets. No VPNs. No private or encrypted messages. No anonymous online accounts. No trade secrets. No confidential data. Any and all data will be available and open to the Chinese government.
- Noria — a new streaming dataflow system designed to act as a fast storage back end for read-heavy web applications. […] It acts like a database, but precomputes and caches relational query results so that reads are blazingly fast. Noria automatically keeps cached results up to date as the underlying data, stored in persistent base tables, change. Noria uses partially stateful dataflow to reduce memory overhead, and supports dynamic, runtime dataflow and query change.
