Preventing AI Plagiarism With .ASS Subtitling

Around two years ago, the world was inundated with news about how generative AI or large language models would revolutionize the world. At the time it was easy to get caught up in the hype, but in the intervening months these tools have done little in the way of productive work outside of a few edge cases, and mostly serve to burn tons of cash while turning the Internet into even more of a desolate wasteland than it was before. They do this largely by regurgitating human creations like text, audio, and video into inferior simulacrums and, if you still want to exist on the Internet, there’s basically nothing you can do to prevent this sort of plagiarism. Except feed the AI models garbage data like this YouTuber has started doing.

At least as far as YouTube is concerned, the worst offenders of AI plagiarism work by downloading the video’s subtitles, passing them through some sort of AI model, and then generating another YouTube video based off of the original creator’s work. Most subtitle files are the fairly straightfoward .srt filetype which only allows for timing and text information. But a more obscure subtitle filetype known as Advanced SubStation Alpha, or .ass, allows for all kinds of subtitle customization like orientation, formatting, font types, colors, shadowing, and many others. YouTuber [f4mi] realized that using this subtitle system, extra garbage text could be placed in the subtitle filetype but set out of view of the video itself, either by placing the text outside the viewable area or increasing its transparency. So now when an AI crawler downloads the subtitle file it can’t distinguish real subtitles from the garbage placed into it.

[f4mi] created a few scripts to do this automatically so that it doesn’t have to be done by hand for each one. It also doesn’t impact the actual subtitles on the screen for people who need them for accessibility reasons. It’s a great way to “poison” AI models and make it at least harder for them to rip off the creations of original artists, and [f4mi]’s tests show that it does work. We’ve actually seen a similar method for poisoning data sets used for emails long ago, back when we were all collectively much more concerned about groups like the NSA using automated snooping tools in our emails than we were that machines were going to steal our creative endeavors.

Thanks to [www2] for the tip!

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Hackaday Links May 13th 2012

Amazing ass… for a robot

Yep, Japan still has the creepy robotics market cornered. Case in point is this robotic posterior. Don’t worry, they’ve included a dissection so you can see how the insides work too. [via Gizmodo]

Time-lapse camera module results

As promised, [Quinn Dunki] sent in a link to the photo album from her time lapse camera module. In case you missed it, she built it in a Tic Tac container and stuck it to the side of a racecar.

Kinect controlled killbot

Didn’t we learn anything from RoboCop? We could totally see this Kinect controlled robot (which happens to weigh five tons) going out of control and liquefying an unsuspecting movie extra standing near it. [via Dvice]

Laser popping domino balloons

apparently [Scott] has set a world record by using a laser to pop a line of 100 red balloons. We enjoy seeing the size of the 1W laser that does the popping… it can’t be long now before we get a hold of handheld laser pistols. [via Gizmodo]

Laser balloon targeting

If that last one was a bit of a let down, you might enjoy this automatic targeting system more. The blue triangle shaped icon is setting a target, the amber triangles have already been targeted. Once all the balloons are identified a laser quickly zaps each in order. Quite impressive, although no details have been provided. [Thanks everyone who sent in a link to this]

http://gizmodo.com/5909007/we-hope-lasers-popping-hundreds-of-balloons-is-the-new-dominos-fad