Screen what comes in, and what went out
Assistant fingerprints, excess vocabulary, tortured phrases, invented references, reused or AI-declared pictures — with the evidence, for submissions, for your recent output as the public record shows it, and for reviewer reports that look alike.
Use it as a policy, not a verdict
A strong signal is a reason for a person to look, never a rejection on its own: ask the author to explain the flagged passages, to supply original figures, to verify the flagged references. Write that into your ethics page: "submissions are screened for AI-writing, paper-mill and image signals; findings are put to the authors before any decision". The Journal Audit now checks for exactly that statement, for an AI-use policy and for an image-integrity policy.
Screening at submission, automatically
With the institution or partner API key, an OJS hook or a script can post each new submission's text and get the same signals back as JSON: https://partners.smartscholars.in/api.php?action=screen. The API page shows the request and the answer.
What it cannot tell you
Whether a clean-looking text was written by a person — no tool can, and the ones that claim to misfire on technical English. Whether a figure that carries no metadata was generated. Whether a reviewer is real. It tells you what the text, the references and the files themselves show, with the rule and the source, so that your decision is yours and can be explained to the author.
