Can You Still Trust a Photo?
04 Can you still trust a photo?
Less than most tools admit. Even strong AI-image detectors lose significant accuracy the moment a photo is recompressed or re-uploaded - which is what happens to nearly everything shared online. One Microsoft study found people themselves correctly told real from AI-generated images only 62% of the time - barely better than a coin flip. There's no reliable "is this real" button, from us or anyone honest about it. What this tool does instead: show the visible evidence - odd compression, mismatched thumbnails, duplicated regions - clearly labeled as a hint, never a verdict.
Go deeper: how detection actually works, and where it breaks down
Three broad approaches exist, and each has a real blind spot:
- Classifier-based detection - a model trained to recognize statistical fingerprints left by specific generators (Midjourney, DALL-E, Stable Diffusion, etc.). Falls behind every time a new generator ships, since it's only ever seen what it was trained on.
- Forensic pixel analysis - error level analysis, noise-consistency checks, copy-move detection. This is the category this tool's optional "forensic hints" belongs to: real signal, but ordinary JPEG artifacts and repetitive real textures trigger the same patterns as genuine tampering.
- Cryptographic provenance (C2PA/SynthID) - the most reliable in principle, since it doesn't guess. The catch: it only covers images from participating tools, is metadata-fragile (C2PA) or requires the original vendor's verifier (SynthID), and proves nothing about images that were never signed in the first place - including most open-source generator output.
Independent 2026 benchmarks found accuracy figures in the 90s look strong on pristine test images, but drop meaningfully the moment an image has been compressed, screenshotted, or run through an upscaler - exactly the condition every image shared online is actually in by the time you see it.
How this tool's own forensic hints fit into that picture
The "forensic hints" feature (Pro tier) runs error level analysis and a coarse copy-move check - the same category of technique described above. It ships with the same honesty this whole page is arguing for: every result carries a caveat that's part of the data structure itself, not an afterthought in the UI, and neither technique claims to detect AI generation specifically. It's here to point at something worth a second look, the same way a smoke detector points at "check this," not "this is definitely a fire."
Further reading
Malwarebytes on Meta's Muse Image feature and the sources linked from the AI-training page cover the provenance side of this in more depth.