In this paper, we present a novel method for detecting fake and Large Language Model (LLM)-generated profiles in the LinkedIn Online Social Network immediately upon registration and before establishing connections. Early fake profile identification is crucial to maintaining the platform's integrity since it prevents imposters from acquiring the private and sensitive information of legitimate users and from gaining an opportunity to increase their credibility for future phishing and scamming activities. This work uses textual information provided in LinkedIn profiles and introduces the Section and Subsection Tag Embedding (SSTE) method to enhance the discriminative characteristics of these data for distinguishing between legitimate profiles and those created by imposters manually or by using an LLM. Additionally, the dearth of a large publicly available LinkedIn dataset motivated us to collect 3600 LinkedIn profiles for our research. We will release our dataset publicly for research purposes. This is, to the best of our knowledge, the first large publicly available LinkedIn dataset for fake LinkedIn account detection. Within our paradigm, we assess static and contextualized word embeddings, including GloVe, Flair, BERT, and RoBERTa. We show that the suggested method can distinguish between legitimate and fake profiles with an accuracy of about 95% across all word embeddings. In addition, we show that SSTE has a promising accuracy for identifying LLM-generated profiles, despite the fact that no LLM-generated profiles were employed during the training phase, and can achieve an accuracy of approximately 90% when only 20 LLM-generated profiles are added to the training set. It is a significant finding since the proliferation of several LLMs in the near future makes it extremely challenging to design a single system that can identify profiles created with various LLMs.

We have all seen artificial intelligence's capabilities when it comes to creating images. However, cybercriminals are using it to create profile pictures of non-existent people and combine them with job descriptions and other information stolen from real profiles on LinkedIn to create fake profiles. These profiles are using uniquely generated photos, which makes them harder to detect. Thousands of these fake profiles indicate a new trend, however, researchers are confused about the motive behind this new trend. According to the most common idea, these profiles are attempting to access various invite-only LinkedIn groups.

Two Stanford researchers have fallen down a LinkedIn rabbit hole, finding over 1,000 fake profiles using AI-generated faces at the bottom. Renée DiResta and Josh Goldstein from the Stanford Internet Observatory made the discovery after DiResta was messaged by a profile reported to belong to a "Keenan Ramsey". It looked like a normal software sales pitch at first glance, but upon further investigation, it became apparent that Ramsey was an entirely fictitious person. While the picture appeared to be a standard corporate headshot, it also included multiple red flags that point to it being an AI-generated face like those generated by websites like This Person Does Not Exist. DiResta was specifically tipped off by the alignment of Ramsey's eyes (the dead center of the photo), her earrings (she was only wearing one) and her hair, several bits of which blurred into the background.

A new British dating app is promising to eradicate Tinder Swindler-style romance fraud, which cost duped daters almost £100m last year, by ensuring that all members complete biometric ID verification before they digitally mingle. Fluttr, which claims to be the first UK online dating app to use such technology to improve user safety, is launching on Valentine's Day in the hope of getting a boost from singletons looking to change their relationship status. The issue of romance fraud, catfishing and fake profiles has been put into the spotlight following the release of Netflix documentary The Tinder Swindler, which tells the true story of a man who went to extraordinary lengths to scam women for millions after meeting them online. The pandemic, when online dating was the only mixing that was allowed, has driven a huge surge in scams costing those duped £92m in the UK last year. "We want to rid the world of Tinder Swindlers and create a safe space free from the fake profiles used to defraud, catfish and abuse online daters," said Rhonda Alexander, the chief executive and co-founder of Fluttr.

I explain Artificial Intelligence terms and news to non-experts. Most of us are convinced that we can dissociate humans from machines, but is it really the case? This study dives into this question using AI-generated profiles on dating apps. Would you swipe right for an AI? Let's find out. This AI Reads Your Brain to Generate Personally Attractive Faces, https://youtu.be/-ZibKcoW-7k

Have you ever considered the possibility that the faces you see on the internet are not even real! The reviews that you read online, the face which you trust on a product's review, the comments and posts shared by people that you may follow and even idealize may not even be real! Yes, in the growing era of digitalization, which has skyrocketed during this pandemic, most of us would not be even surprised to know that Artificial Intelligence (AI) is busy making fake people. In the world where there is an emphasis on publishing, advertising, buying, making friends get influenced by people they meet virtually, AI is being used to create fake profiles with fake faces and treating them as real human beings. The worst part is that they seem so real, that even the smartest of us cannot easily distinguish between a real human face and the one created by AI.

'Deepfakes' is the name given to video and audio developed by artificial intelligence (AI), resembling something, someone-- or someone doing something-- that didn't, in fact, occur. Advances in deep-learning and AI continue to make deepfakes more realistic, to the extent that in many cases it's becoming very difficult to distinguish what is real, and what is generated by AI. Give it a go on this website, and see if you can determine which is a real photo, and which is computer generated. With the presence of deepfakes doubling within the last year, and the technology continuously advancing, there are clear concerns surrounding the various ways they could be used. Many predict that deepfakes could provide a dangerous new medium for information warfare, helping to spread misinformation or'fake news'.

Being able to spell, checking your profile for typos and using correct grammar could determine whether you get a match on a dating site, a new study has revealed. Researchers worked with members of an online dating service and asked them to read fake profiles that had language errors as well as ones that had no mistakes. The study of more than 800 people on a dating site in the Netherlands found that errors such as writing'teh' for'the' and putting capital letters in the wrong place suggested sloppiness and a lack of attention to detail. If a profile has spelling mistakes and grammatical errors they were more likely to be perceived as less intelligent and so were less likely to be a match. 'In the case of online dating, inattentiveness can be interpreted as a lack of effort and interest in putting time and effort in constructing a dating profile.

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. 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 Romance scams, where criminals create phony profiles to trick love-lusting victims into sending them money, are on the rise.

Cambridge Analytica was caught tampering with elections by exploiting Facebook, but chances are that this is the tip of the iceberg, and that many others, including scammers and ID thieves, are also exploiting Facebook and other social networks. One way that they do this is as follows. Also, scammers use dozens if not hundreds of IP addresses to create these numerous fake accounts. They do it by recruiting an army of drone workers paid peanuts, or via a Botnet, or recycled or non-static IP addresses, or proxy servers. The smartest ones might even use computer viruses to create Facebook accounts in the background on your hijacked computer (thus via your IP address), without you being aware of it.