I think weighing the likely reward against the effort of creating a fake image is going to be helpful as we try to identify strategies to detect and exclude fake images (and thereby avoid eroding identifiers’ confidence that they’re reviewing real observations of nature).
It seems the motivation of users adding fake observations mostly falls into two group: perceived Internet fame and getting a passing grade on a student project.
It’s certainly plausible that the fame-seekers have sufficient motivation to use an AI image generator or photoshop to add fake observations of rare creatures, cryptozoology and the like. I’d guess the probability of this behavior would be correlated with the notability of the organism and that might indicate the best way to combat it. We should approach every observation of an Ivory Billed Woodpecker or Spix’s Macaw with a healthy skepticism; and conversely we don’t need to exhaustively analyze every new House Sparrow or Mallard.
One reason not to increase the gamification of iNat is to avoid motivating users to create fake observations for more ordinary species simply in order to increase their species count.
For students under duress to add observations, the first thing should be to try to educate the instructor that requiring students to add observations with little guidance is actively harmful. (iNat Educator’s Guide, useful forum thread). Once students are assigned work that involves adding observations, it can be a mistake to make it too challenging. Certainly, it’s worth encouraging students to focus on wild organisms, but so long as the boundaries are not too narrow, that can still be accomplished easily in most cities (birds, insects, weeds). The more challenging the project, the more likely a few will be motivated to create fake content.
I do think it would be worthwhile for iNat to include some automated image checks to provide guidance to identifiers. Adding the following three would be very useful:
- Compare checksum against existing iNat images. (Detects unintentional duplicates and also image theft and sharing.)
- Compare using image search engines (e.g. Google Images, TinEye). Given that these are commercial providers, this may have to be via generating a link to make the comparison process easier.
- Compare against an AI-checker algorithm. I’d envisage that this might generate a confidence score that an identifier might use to determine whether to investigate further.