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Conspirador Norteño

@conspirator0

Dec 23, 2021 • 8 tweets • 7 min read • Read on X

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@ZellaQuixote

Oh look, a bunch of accounts named George with GAN-generated profile pics are tweeting identical tweets. #HolidayShenaniGANs

(GAN = "generative adversarial network", the technique used by thispersondoesnotexist.com to generate fake face pics)

cc: @ZellaQuixote

@CeloLaunch

The spammy Georges are part of a larger network of 2500 newly-created accounts promoting cryptocurrency launchpad site @CeloLaunch. Between them, these 2250 accounts have just 25 unique first names and 50 unique last names. All have zero likes, zero followers, and 1 or 2 tweets.

@CeloLaunch

Most of these accounts have tweeted exactly twice: a duplicated tweet promoting @CeloLaunch, and a retweet of a December 12th @CeloLaunch tweet (a few missed either the duplicate tweet or the retweet). All tweets were sent (allegedly) via the Twitter web app.

@CeloLaunch

All 2250 accounts in the network follow @CeloLaunch and at most one other account. Those most frequently followed (other than @CeloLaunch) are @ZelenskyyUa, @Ukraine, @elonmusk, and @ekrem_imamoglu.

All 2250 accounts use GAN-generated profile pics. Unmodified GAN-generated face pics have the telltale feature that the major facial features are in the same position on every image*, which becomes evident when the images are blended together.

(*for now; StyleGAN 3 changes this)

Despite the fact that one can produce as many unique GAN-generated face images as one's heart desires, the operator of this network in many cases reused the same image on multiple accounts. #YouHadOneJob

As tends to be the case with large networks of accounts with GAN-generated pics, some of the accounts ended up with glitchy images ranging from hilarious to utterly creepy.

https://twitter.com/conspirator0/status/1457051680902717446

Some other examples of common signs that a "face" may be GAN-generated in this thread:

https://twitter.com/conspirator0/status/1457051680902717446

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More from @conspirator0

Conspirador Norteño

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Jun 6

Some thoughts on perennial pitfalls in news coverage of social media manipulation that frequently result in reporting on fake accounts/bots/etc being far less accurate and informative than it ought to be...

cc: @ZellaQuixote
conspirator0.com/p/journalism-i…

The most common problem with news articles about fake accounts: failure to include any examples of fake accounts or evidence of their inauthenticity. Any or all of these headlines might be accurate, but you can't tell from the articles, due to absence of evidence.

A related issue: articles like the "Nearly Half of Biden/Trump's Followers Are Fake" and "Nearly Half Of Accounts Tweeting About Coronavirus Are Bots" pieces base their numbers on closed-source third party tools, which may or may not actually be detecting anything useful.

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Apr 26

Does thanking, praising, or insulting an LLM-based chatbot affect the speed or accuracy of its responses to questions involving basic arithmetic? Let's find out!

cc: @ZellaQuixote
conspirator0.com/p/how-to-talk-…

For this experiment, Meta’s Llama 3.1 model was asked to add and multiply random numbers between 10 and 100, with six different wordings: polite, rude, obsequious, urgent, and short and long neutral forms. Each combination of math operation and wording was tested 1000 times.

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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.

Read 5 tweets

Conspirador Norteño

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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.

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Conspirador Norteño

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Jan 24

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.

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Conspirador Norteño

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Jan 10

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.