Yes, good idea. I suggest the following:

• Uploaded by a new user (less than 20 uploads): +1
• Uploaded by an account without any right (not autopatroller): +1
• File without complete EXIF data: +1
• File size less than 200 KB: +1
• File size less than 100 KB: +2
• File dimension less than 1 Mpixels: +1
• File depicting a person: +1
• File depicting a logo: +1

Files with 5 points or more have a very high chance to be copyright violations.

As a proof of concept, I wrote during the Wikimania-Hackathon-2019 a script to scan new images with a rule based approach based on earlier discussion in https://commons.wikimedia.org/wiki/Commons:Village_pump/Copyright/Archive/2019/07#Copypatrol_for_images

Process:

1. New files are processed based on rules suggested @AlexisJazz good comment (but it doesn't implement all the suggested rules).
2. It then use simple random forest model to provide a score for attention (training and test set are based on https://commons.wikimedia.org/wiki/Category:Copyright_violations and randomly selected files, features are all EXIF data, all templates and user groups of the uploader).

Next steps would be:

1. community evaluation of the generated reports and fine tunning the script with more rules
2. running them periodically in toolserver

Once it get mature enough we can connect it to google search, selecting the top X ranked images for search where X will be defined by the credits we have for search in google (T31793).

@eranroz: Hi, are you still working on this? Should this task still remain open?

Closing as this task was created years after the CWS one, and is mostly about a specific potential implementation of T120453, Future discussion about training a model can occur in the 2015 task. Also note that training ML models can be costly themselves.