If your interests include OnlyFans, pro-China propaganda, and social media accounts with artificially generated faces, then this is the botnet for you. (There's also cryptocurrency spam involved because of course there is.) #FridayShenaniGANs
This network consists of (at least) 1627 Twitter accounts created between April 30th and May 20th, 2023. All of them follow at least two of the following accounts: @AoTJewels, @BlackYellow, @Fenerbahce, and @elonmusk ,and tweet via Twitter for Android or the Twitter Web App.
The 1627 accounts in this network all use GAN-generated faces as their profile pics. All of these images have neutral backgrounds and are 255x255 pixels (Twitter default is 400x400).
(GAN = "generative adversial network", the technology used to generate the "face" images)
Unmodified StyleGAN-generated face images have the interesting property that the major facial features (particularly the eyes) are in the same position on every image. This becomes obvious when one blends multiple images together (such as the 1627 "faces" used by the network).
The accounts in this network tweet in multiple languages, with English being the most frequent (66.1% of tweets), followed by Chinese (16.1%). Slightly over half of their content is retweets (56.8%); the content retweeted is mostly NFT/cryptocurrency-related.
The network's content is repetitive, with large numbers of accounts tweeting identical or similar tweets, but varied in theme. The most common types of English tweets are similarly-worded porn tweets and famous quotes.
The network's repetitive Chinese tweets, on the other hand, are often political (although there's some porn in there too). Topics include the Australian Strategic Policy Institute, Guo Wengui, Steve Bannon, and Yan Li-Meng, all of whom the network's tweets portray as anti-China.
Several of the accounts in this network also recently tweeted propaganda about Moldova. More info in this thread and blog post (H/T @z3dster). medium.com/@mitchchaiet/m…
Tips on detecting StyleGAN-generated faces (note that some of these do not apply to output from text-to-image models such as Stable Diffusion or Midjourney):
Just for fun, I decided to search Amazon for books about cryptocurrency a couple days ago. The first result that popped up was a sponsored listing for a book series by an "author" with a GAN-generated face, "Scott Jenkins".
cc: @ZellaQuixote
Alleged author "Scott Jenkins" is allegedly published by publishing company Tigress Publishing, which also publishes two other authors with GAN-generated faces, "Morgan Reid" and "Susan Jeffries". (A fourth author uses a photo of unknown origin.)
As is the case with all unmodified StyleGAN-generated faces, the facial feature positioning is extremely consistent between the three alleged author images. This becomes obvious when the images are blended together.
The people in these Facebook posts have been carving intricate wooden sculptures and baking massive loaves of bread shaped like bunnies, but nobody appreciates their work. That's not surprising, since both the "people" and their "work" are AI-generated images.
cc: @ZellaQuixote
In the last several days, Facebook's algorithm has served me posts of this sort from 18 different accounts that recycle many of the same AI-generated images. Six of these accounts have been renamed at least once.
The AI-generated images posted by these accounts include the aforementioned sculptures, sad birthdays, soldiers holding up cardboard signs with spelling errors, and farm scenes.
The common element: some sort of emotional appeal to real humans viewing the content.
As Bluesky approaches 30 million users, people who run spam-for-hire operations are taking note. Here's a look at a network of fake Bluesky accounts associated with a spam operation that provides fake followers for multiple platforms.
cc: @ZellaQuixote
This fake follower network consists of 8070 Bluesky accounts created between Nov 30 and Dec 30, 2024. None has posted, although some have reposted here and there. Almost all of their biographies are in Portuguese, with the exception of a few whose biographies only contain emoji.
The accounts in this fake follower network use a variety of repeated or otherwise formulaic biographies, some of which are repeated dozens or hundred of times. Some of the biographies begin with unnecessary leading commas, and a few consist entirely of punctuation.
It's presently unclear why, but over the past year someone has created a network of fake Facebook accounts pretending to be employees of the Los Angeles Dodgers. Many of the accounts in this network have GAN-generated faces.
cc: @ZellaQuixote
This network consists of (at least) 80 Facebook accounts, 48 of which use StyleGAN-generated faces as profile images. The remaining 32 all use the same image, a real photograph of a random person sitting in an office.
As is the case with all unmodified StyleGAN-generated faces, the main facial features (especially the eyes) are in the same position on all 48 AI-generated faces used by the network. This anomaly becomes obvious when the faces are blended together.
None of these chefs exist, as they're all AI-generated images. This hasn't stopped them from racking up lots of engagement on Facebook by posting AI-generated images of food (and occasional thoughts and prayers), however.
cc: @ZellaQuixote
These "chefs" are part of a network of 18 Facebook pages with names like "Cook Fastly" and "Emily Recipes" that continually post AI-generated images of food. While many of these pages claim to be US-based, they are have admins in Morocco per Facebook's Page Transparency feature.
Between them, these 18 Facebook "chef" pages have posted AI-generated images of food at least 36,000 times in the last five months. Not all of the images are unique; many have been posted repeatedly, sometimes by more than one of the alleged chefs.
Can simple text generation bots keep sophisticated LLM chatbots like ChatGPT engaged indefinitely? The answer is yes, which has some potentially interesting implications for distinguishing between conversational chatbots and humans.
For this experiment, four simple chatbots were created:
• a bot that asks the same question over and over
• a bot that replies with random fragments of a work of fiction
• a bot that asks randomly generated questions
• a bot that repeatedly asks "what do you mean by <X>?"
The output of these chatbots was used as input to an LLM chatbot based on the 8B version of the Llama 3.1 model. Three of the four bots were successful at engaging the LLM chatbot in a 1000-message exchange; the only one that failed was the repetitive question bot.