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InfoSec News Nuggets 6/12/2020

Interpol arrests flamboyant Nigerian socialite, Ray Hushpuppi for alleged $35 million COVID-19 Internet scam

Eyewitnesses in Dubai said Hushpuppi and his friend where ‘allegedly surrounded by the International police and FBI on the grounds of being fraud suspects’. Hushpuppi who said he will not come back to Nigeria has been accused of being an Internet fraudster because of his flamboyant and expensive lifestyles without a convincing business and source of his lavish lifestyle. According to the unconfirmed charges by the Interpol, Hushpuppi’s Internet fraud gang were accused of exploiting COVID-19 benefits to scam the American government by diverting the benefits from the unemployed victims. The report alleged that the gang must have stolen over $35 million in unemployed benefits. However, the latest reports suggested that Hushpuppi’s Internet fraud gang could have defrauded American states and tax payers close to $100 million.

 

Amazon bans police from using its facial recognition technology for the next year

Amazon is announcing a one-year moratorium on allowing law enforcement to use its controversial Rekognition facial recognition platform, the e-commerce giant said on Wednesday. The news comes just two days after IBM said it would no longer offer, develop, or research facial recognition technology, citing potential human rights and privacy abuses and research indicating facial recognition tech, despite the advances provided by artificial intelligence, remains biased along lines of age, gender, race, and ethnicity.

 

INCREASED USE OF MOBILE BANKING APPS COULD LEAD TO EXPLOITATION

The FBI advises the public to be cautious when downloading apps on smartphones and tablets, as some could be concealing malicious intent. Cyber actors target banking information using banking trojans, which are malicious programs that disguise themselves as other apps, such as games or tools. When the user launches a legitimate banking app, it triggers the previously downloaded trojan that has been lying dormant on their device. The trojan creates a false version of the bank’s login page and overlays it on top of the legitimate app. Once the user enters their credentials into the false login page, the trojan passes the user to the real banking app login page so they do not realize they have been compromised.

 

‘Bot or Not?’ – a game to train us to spot chatbots faking it as humans

Bot or Not is an online game that pits people against either bots or humans. It’s up to players to figure out which they’re engaging with in the 3-minute game, in which they’re forced to question not only whether their opponent is human but exactly how human they themselves are. The creators of Bot or Not – a Mozilla Creative Awards project that was conceived, designed, developed and written by the New York City-based design and research studio Foreign Objects – say that these days, bots are growing increasingly sophisticated and are proliferating both online and offline. It’s getting tougher to tell who’s human, which can come in handy in customer service situations but is a bit scary when you think about scam bots preying on us on Tinder and Instagram, or corporate bots that try to steal your data.

 

New benchmark measures gender bias in speech translation systems

In machine translation, gender bias is at least partially attributable to the differences in how languages express female and male gender. Those with a grammatical system of gender, such as Romance languages, rely on a copious set of inflection and gender agreement devices that apply to individual parts of speech. That’s untrue of English, for instance, which is a “natural gender” language — it reflects distinction of sex only via pronouns, inherently gendered words (e.g., “boy,” “girl”), and marked nouns (“actor,” “actress.”) AI translation systems that fail to pick up on the nuances threaten to perpetuate under- or misrepresentation of demographic groups. Motivated by this, the researchers created MuST-SHE, a multilingual test set designed to uncover gender bias in speech translation.

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