Русский язык
Has Some Utility
Not definitive
Response from developer
A photo checked straight off an iPhone camera roll, taken that same morning, doesn't come back 40-50. The only way we've seen that split happen is when a file reaches the detector with its capture metadata stripped or altered somewhere along the way, so whatever you tested had been through more than your wife's camera. The numbers themselves work like this: when the original capture metadata is there, the call is decisive, and when it isn't, we score the pixels alone using our SOTA Image detection model and print whatever probability that evidence actually supports. Plenty of detectors would round a 55 up to a 99 because it looks better in a screenshot. We think that amounts to lying with statistics. Share the photo directly from your camera roll and run it again and compare the results.
Garbage
Response from developer
Thanks for the review. Slop or Not checks images in layers. Files carrying AI provenance metadata, meaning C2PA, IPTC, or Google's SynthID tags, get flagged as AI, and in our internal tests that catch rate is 100%. Files without metadata go to OmniAID, our own detection model, which scores 90% on unwatermarked AI images in those same tests; results can shift as new generators appear. So yes: edit a real photo with an AI tool and the tool writes AI provenance into the file. The detector flags it because the file says so. That part of your review checks out. The other half doesn't. "Showed AI ones and says it's real" is disingenuous: it describes behavior we cannot reproduce. If it really happened, email the photo to support@slopornot.ai and we will fix it for every customer after you. The same detector runs free at slopornot.ai. Anyone reading this can test your claim themselves in seconds.
Did not detect reliably
2. Accuracy claims are misleading, given that confidence scores vary, and this score was not very high on an AI abstract I tested. People will falsely assume a 75% confidence score falls within the 95% accuracy threshold.
3. The possibility of false positives is not disclosed, which could lead to overconfidence and unfair penalization of students, if present at all.
No way to attach text, pdf, or doc files on iOS.
Response from developer
Quick note on the numbers since they get conflated a lot. Per-class accuracy on our test set and per-image confidence aren't measuring the same thing. 75% confidence on a sample doesn't contradict 95% per-class accuracy. False positive risk is called out in the app itself and on the store page, and PDF or document attachment isn't on the roadmap because the iOS Share Sheet already handles those flows. If you've got a sample you want us to look at, mail support@slopornot.ai and we'll dig in. EDIT: For anyone reading this, the review was written in bad faith. The Slop Or Not text detector scores 99.09% (@ 1% FPR) on the RAID benchmark. https://raid-bench.xyz/leaderboard
