Three ways to detect an AI-generated image. Understanding all three is the point.
Method 1: ML classification. Train on a dataset. Run inference. Fails on unseen generators and lightly post-processed outputs. This is what most "AI detector" tools do.
Method 2: Explicit declaration. Three forms in practice:
- Platform labels (LinkedIn Content Credentials badge, Meta AI label, YouTube disclosures)
- C2PA digitalSourceType: trainedAlgorithmicMedia in a COSE-signed manifest
- Invisible watermarks at generation time (Google SynthID, Midjourney, Firefly, Meta)
Reliable when present. Hard limit: requires the creator to declare it. Most AI images in circulation have no Method 2 signal.
Method 3: Forensic analysis. Signals in the file's physical structure — nothing to do with visual appearance, everything to do with how the file was made.
- High-pass filter residual: camera sensor noise is quantum physics;
diffusion model upsampling artefacts are structured and periodic
- Wavelet HF/LL energy ratio: AI images are characteristically smooth
in high-frequency sub-bands
- NSS Benford analysis: DCT coefficient leading digits deviate from
Benford's Law
- ELA: compression history differs from camera captures
- Metadata absence: no MakerNote, no lens serial, no device calibration fields
No retraining needed for new generators. No reliance on creator honesty.
Full article: https://kennethbspringer.au/how-to-forensically-detect-ai-generated-images-no-detection-model-required/
snapWONDERS runs all three where signals are present. No account needed.
#OSINT #digitalforensics #infosec #aidetection #imageforensics #AI
