Checking DALL·E / GPT image output
Check DALL·E and GPT images before they represent your brand
OpenAI’s image models are strong at following instructions, which tempts teams to trust the output as final. But instruction-following is not correctness: small text still garbles, faces in crowds still smear, and a generated product shot can quietly disagree with the real product. Chekr verifies the image you are actually about to publish.
What typically goes wrong in DALL·E / GPT images
- Small print, UI text and packaging copy that degrades even when headlines survive
- Background faces and hands in group scenes
- Logos and brand marks that drift from the real asset
- Product details that differ from the actual SKU — stitching, ports, proportions
- Compositing seams when images are edited or extended
How Chekr checks it
Chekr scans the exported image with all nine checks — text, anatomy, artifacts, physics, coherence, lighting, brand rules, provenance and reverse-image IP risk — and pins every finding with severity and confidence. It reads what the text actually says, not what the prompt asked for, which is how prompt-faithful-but-wrong output gets caught.
For teams generating at volume, the same scan runs through the API: send each render as it lands, gate publication on the integrity score, and keep the findings as an audit trail.
Frequently asked
GPT image models render text well — why check it?
Better is not reliable: long strings, small sizes and non-English diacritics still fail at a meaningful rate. The scan costs seconds; a misspelled price in a paid placement costs more.
Does Chekr detect that an image came from DALL·E?
Where the file carries provenance metadata (such as C2PA Content Credentials), the forensics check reads and reports it. Detection is not the goal — verifying the creative you already know is generated is.
Can I automate checks in a generation pipeline?
Yes — the API accepts uploads and returns findings and a score per image, so you can scan every render automatically and only route failures to a human.