If you're interested in like this line of attack, you can also use similar techniques to defeat models that are trained to do object detection (like, for example, the ones that detect the location of your license plate) using adversarial noise attacks.

The short version is, if you have a network that does detection, you can run inference with that network on images that have been altered by another network and have the second network use the confidence of the detection network in its loss function. The second model can be trained to create noise, which looks innocuous to human eyes, that maximally disrupts the segmentation/object detection process of the target/detection network.

You could then print this noise on, say, a transparent overlay and put it on your license plate and automated license plate readers (ALPRs) would not be able to detect/read your plates. Note: Flock is aware of this technique and has lobbied state lawmakers to make putting anything on your plate to disrupt automated reading illegal in some places, check your laws.

Benn Jordan has actually created and trained such a network video here: https://www.youtube.com/watch?v=Pp9MwZkHiMQ

And also uploaded his code, PlateShapez to github: https://github.com/bennjordan

In states where you cannot cover your license plate you're not restricted from decorating the rest of your car. You could use a similar technique to create bumper stickers that are detected as license plates and place them all over your vehicle. Or, even, as Benn suggested, print them with UV ink so they're invisible to humans but very visible to AI cameras who often use UV lamps to provide night vision/additional illumination.

You could also, if you were so inclined, generate bumper stickers or a vinyl wrap which could make the detector be unable to even detect a car.

Adversarial noise attacks are one of the bigger vulnerabilities of AI-based systems and they come in many flavors and can affect anything that uses a neural network.

Another example (also from the video) is that you can encode voice commands in plain audio which, to the user is completely transparent but a device (like Alexa or Siri) will hear it as a specific command ("Hey Siri, unlock the front door"). Any user-generated audio that you encounter online can have this kind of attack encoded in it, the potential damage is pretty limited because AI assistants don't really control critical functions in your life yet... but you should probably not let your assistant listen to TikTok if it can do more than control your home lighting.