Neural Network to Forge Fingerprints

DeepMasterPrints can be used as a key for hacking biometric identification systems
16 November 2018   1370

Researchers at New York University have developed a generative-adversary network for prototyping fingerprints. Images of these prints, called DeepMasterPrints, can be used as a key for hacking biometric identification systems.

The principle of creating DeepMasterPrints is to use two properties of fingerprints and biometric systems.

Many scanners do not read the entire print. They process a part of it, and then compare it with the exact same part of the print from the database. Thus, a fake print should correspond only to a part of the original one.

Secondly, the researchers noted that some features of prints are repeated. This means that an artificial footprint that contains a set of common features will correspond to several genuine footprints at once.

DeepMasterPrints
DeepMasterPrints

The study showed that at a system tolerance level of 0.1%, artificial fingerprints can forge up to 23% of all fingerprints from the database. An error of 1% allows neural networks to fake up to 77% of prints.

Experts compare the use of DeepMasterPrints with a brute-force attack, when an attacker sorts through all the passwords that may be appropriate in this case.

BNC to Monitor BTC Community's Mood

The system called Twitter Sentiment analyzes over 34M BTC-related Twitter posts each week, using AI to track the mood of the community
22 January 2020   202

Blockchain-based New Zealand-based research firm Brave New Coin (BNC) has unveiled a new system for measuring the mood of the Bitcoin community based on Twitter messages.

According to BNC, the new Twitter Sentiment rating system analyzes over 34 million BTC-related Twitter posts each week. The company uses artificial intelligence (AI) algorithms that look for records containing the words bitcoin, $ BTC and BTC and others.

BNC notes that user sentiment continues to be a “significant” factor in the price and dynamics of digital assets, and a new technique has been developed to track these sentiments. According to the BNC, it took 18 months to launch the Bitcoin Twitter Sentiment. The data obtained is divided into seven categories - Opinion, Technical Information, Inside the Network, Advertising, Bots, Macros and Hacking.

For the week ending January 17, the most common entries were in the Opinion category - their number was 30.42% of all data received. In second place was the category Technical Information, and in third inside the network (includes information on mining and hashrate).

BNC spokeswoman Pierre Ansaldi said that during the first quarter of this year, the company will also launch community sentiment analysis tools for other crypto assets.