None of these cats exist. All are GAN-generated images obtained from thiscatdoesnotexist.com. Can we come up with a way to detect GAN-generated cat pics? #CaturdayShenaniGANs
(GAN = "generative adversarial network", the AI technique used to create the images)
For this project, we used the following datasets (all images 512x512):
• 2000 GAN-generated cats from thiscatdoesnotexist.com
• 1195 real cat faces, cropped from images at kaggle.com/crawford/cat-d…
• a test set of 1000 GAN-generated and 1000 real cats (same sources as above)
Unlike the GAN-generated human face pics provided by thispersondoesnotexist.com etc, the placement of the major facial features on the GAN-generated cat pics from thiscatdoesnotexist.com varies from image to image. There are other anomalies in the fake cat pics, however. . .
We blended 2000 GAN-generated cat pics and altered the color scheme so that each pixel's brightness corresponds to how different that pixel's color is from the average color of the image. This reveals various horizontal and vertical bands in the GAN-generated cat images.
We can emphasize this banding further by blending all the pixels in the same row or column of the image and calculating the difference between adjacent values. This results in a distinctive pattern of peaks every other pixel and slightly less visible peaks every 8 pixels.
The same pattern shows up (albeit less visibly) when we apply the same process to a single GAN-generated cat image. Notably, this pattern is largely absent from real cat face pics, both single and blended.
We used these results to train a simple neural network to classify cat face images as "real" or "GAN-generated". Results are encouraging - within a few hours of training we ended up with a model that achieved 94.2% accuracy on 2000 images it hadn't been trained on.
Here's the code we used for both training and classification. It is presently hardcoded to use 512x512 pixel images (the size generated by thiscatdoesnotexist.com) and will likely require tweaking for other image sizes. pastebin.com/hqPtD5CD
A couple of disclaimers:
• This method is unlikely to produce useful results on images that aren't of cat faces.
• If using it on Twitter profile pics, you will want to obtain the full-resolution version of the pics for best results (more detail here: developer.twitter.com/en/docs/twitte…).
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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.
The spammers behind the "Barndominium Gallery" Facebook page have branched out into AI-generated video and started a YouTube channel with the catchy name "AY CUSTOM HOME". The results are just about as craptastic as you'd expect.
In this synthetically generated aerial video of a (nonexistent) barndominium under construction, the geometry of the roof changes, a blue building appears, and a tree vanishes, all in the course of just three seconds.
This AI-generated barndominium features a long AI-generated porch with some chairs on it. Exactly how many chairs there are depends on what angle you look at it from, however, as the chair on the left splits into three chairs as the camera pans.
Some observations regarding @Botted_Likes (permanent ID 1459592225952649221)...
First, "viral posts which don't result in follower growth and have very little engagement in the reply section" is not a useful heuristic for detecting botted likes. Why not?
cc: @ZellaQuixote
"Viral posts that do not result in follower growth" is not a valid test for botting, because posts from large accounts often go viral among the large account's existing followers but do not reach other audiences, resulting in high like/repost counts but little/no follower growth.
"Very little engagement in the reply section" doesn't work for multiple reasons (some topics spur debate and some don't, some people restrict replies, etc)
Hilariously, @Botted_Likes seems to be ignoring their own criteria, as many of the posts they feature have tons of replies.
As with the banned @emywinst account, the @kamala_wins47 account farms engagement by reposting other people's videos, accompanied by bogus claims that the videos have been deleted from Twitter. These video posts frequently garner massive view counts.
@Emywinst @kamala_wins47 The operator of the @kamala_wins47 account generally follows up these viral video posts with one or more replies advertising T-shirts sold on bestusatee(dot)com. This strategy is identical to that used by the banned @emywinst account.
What's up with all these similarly-worded enthusiastic posts about a Pierre Poilievre rally in Kirkland Lake, and are they all from accounts that are less than a month old? (Spoiler: yes, they are.) #Spamtastic
cc: @ZellaQuixote
An X search for "Pierre Poilievre", "Kirkland Lake", and "refreshing" performed on August 4th, 2024 turned up 151 posts from 151 accounts. All are new accounts, with the oldest having been created less than a month ago, on July 7th, 2024. (Some have since been suspended by X.)
The most intense period of activity for this group of accounts was on August 3rd, 2024, when the repetitive posts about the Poilievre rally were posted. Each account also has at least one earlier post on a random topic; some of these older posts seem to cut off abruptly.