1930s fraud-detection mathematics, still working today — just aimed at a target it was never built for.
Challenge: catch an AI-generated JPEG using nothing but a 1938 statistical law. No neural network involved.
Benford's Law: in real-world numerical data, the leading digit isn't uniformly distributed — 1 shows up ~30% of the time, 9 under 5%. Holds for river lengths, electricity bills, physical constants... and, it turns out, for the AC coefficients in a JPEG's DCT blocks, if the image actually came from a capture-and-compress pipeline.
I run this as one of six checks in snapWONDERS' analyse pipeline: 8×8 DCT on every block, absolute value of the AC coefficients (DC term excluded — it doesn't follow Benford), leading digit extracted, binned against the classic log10(1 + 1/d) curve, then a chi-squared goodness-of-fit test. Above threshold, it's flagged.
It's not a standalone verdict — heavy resampling or repeated re-saves can trip it too, same as a genuinely synthetic image would. So it feeds a combined score alongside five other signals rather than deciding anything alone. But as a zero-training, zero-black-box check, it's a genuinely satisfying one: an accounting fraud-detection tool from 1938 has something real to say about whether a photo actually came out of a camera.
Full technical breakdown (with the formula) → https://kennethbspringer.au/2026/08/05/benfords-law-detecting-ai-generated-images-without-ai/?utm_source=mastodon&utm_medium=social&utm_campaign=article-16
#digitalforensics #OSINT #infosec #aiimages #deepfake #snapWONDERS
