【人工智能应用于约会软件?虚假材料无处可藏】

【人工智能应用于约会软件?虚假材料无处可藏】研究人员开发了一种人工智能算法,可以检测热门约会应用程序上的虚假资料,该算法可用来识别虚假约会资料中重复出现的特征,包括IP地址、被盗信息、照片和语言,科学家们表示,只有1%被标记为伪造的个人资料是真实的,他们相信这个算法有一天会在流行的约会软件或网站上使用。

Never get catfished again: Researchers develop AI algorithm that can detect fake profiles on popular dating apps

  • An algorithm was trained to spot recurring traits used in fake dating profiles
  • This include things like IP addresses, stolen information or photos and language
  • Scientists said only 1% of profiles it flagged as being fake were actually genuine
  • They believe the algorithm could one day be used in popular dating apps or sites 

Scientists have developed an algorithm that can spot dating scams.

A team of researchers trained AI software to 'think like humans' when looking for fake dating profiles.

While the algorithm has only been deployed in a research setting, it could one day be used to protect users on popular dating services like Tinder and Match.com.

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Scientists have developed an algorithm that can spot dating scams. A team of researchers trained AI software to 'think like humans' when looking for fake dating profiles

Scientists have developed an algorithm that can spot dating scams. A team of researchers trained AI software to 'think like humans' when looking for fake dating profiles

The study was conducted by a group of researchers from the University of Warwick and published this week.

Researchers first trained the algorithm by supplying it with profiles that were already deemed fake.

From this, the AI was able to detect recurring elements between the profiles that might indicate it as being fake.

For example, the fake profiles might share the same phone number or IP address, as well as include stolen material, such as someone else's photo or user bio.

Additionally, many of the fake profiles used similar 'stylistic patterns of persuasive messaging,' not unlike the repeated language you might see across spam emails.

After scanning all the fake profiles, the algorithm applied its knowledge to profiles submitted to online dating services and come to a conclusion on the probability of each profile being fake.

In total, only one percent of the profiles it flagged as fake were genuine, according to the University of Warwick.

The report doesn't say how successful it was at flagging genuine profiles or how many profiles it reviewed overall.

Still, the researchers say it bodes well for bringing the software to actual dating apps.

AI and deep machine learning raise ethical concerns about control, privacy, cybersecurity, and is set to trigger job displacements across industries, companies and experts say 

AI and deep machine learning raise ethical concerns about control, privacy, cybersecurity, and is set to trigger job displacements across industries, companies and experts say

'The aim is to further enhance the technique and enable it to start being taken up by dating services within the next couple of years, helping them to prevent profiles being posted by scammers,' the University of Warwick explained.

They believe the algorithm is sorely needed in an industry where romance scams are on the rise.

On Tuesday, the Federal Trade Commission issued a notice saying that 'romance scams,' or scenarios where scammers trick love-lusting internet users into sending them money, cost victims an astonishing $143 million in 2018.

That's up from $33 million the previous year and making it the most costly type of consumer fraud reported to the FTC.

'Online dating fraud is a very common, often unreported crime that causes huge distress and embarrassment for victims as well as financial loss,' Tom Sorrell, a co-author of the study, said in a statement.

'Using AI techniques to help reveal suspicious activity could be a game-changer that makes detection and prevention quicker, easier and more effective, ensuring that people can use dating sites with much more confidence in future.'

文章来源:https://www.dailymail.co.uk/sciencetech/article-6710749/Never-catfished-Researchers-develop-AI-detects-fake-profiles-popular-dating-apps.html


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