Image integrity issues are a documented trigger for desk rejection at major journals, and many of these issues are unintentional, arising during figure preparation or complex workflows. Screening figures with an image analysis tool before submission helps surface potential issues for review and reduces the risk of delays.
Why Are Manuscripts Being Desk-Rejected for Image Problems?
Editors at leading journals now routinely screen submitted manuscripts for image integrity problems before sending them to peer review. The Science family of journals adopted Proofig AI across all six journals specifically to detect altered images at the submission stage. MDPI, one of the largest open-access publishers, signed a multi-year deal with Proofig AI for the same purpose. Driven by a commitment to ensuring research integrity and guaranteeing the quality of published research, MDPI adopted automated image screening across its journals. As Milos Cuculovic, Head of Technology Innovation at MDPI, noted: “From the experts, it seemed Proofig AI was by far the best choice, and here we are: further ensuring research integrity and guaranteeing the high quality of research output published by MDPI.”
When automated screening flags potential image concerns in a submitted manuscript, editors may choose to desk-reject the paper or request clarification and figure corrections before peer review, depending on journal policy and the severity of the issues. The researcher can lose weeks or months of potential review time, sometimes with little indication of what was found. This is not a hypothetical risk. It is a routine part of the editorial workflow at journals that have adopted automated image integrity screening.
How Widespread Are Image Integrity Issues in Submitted Manuscripts?
The scale of the problem is larger than most researchers expect. Industry analyses and image-integrity specialists report that approximately 20–35% of life-science manuscripts submitted to journals are flagged for potential image-related issues during screening, including duplication or manipulation (UKRIO). This figure reflects the pre-publication submission stage, not the published literature.
Even among already-published papers, the numbers are significant. A landmark study by Bik, Casadevall, and Fang (2016) screening over 20,000 published papers found that 3.8% contained problematic image duplication, with about half showing features suggestive of deliberate manipulation. The rest were consistent with honest error.
The retraction landscape reinforces the urgency. A recent Nature analysis reported that the share of articles that end up retracted has roughly tripled over the past decade (Nature). A peer-reviewed analysis of top-20 world-class universities documented that image manipulation was among the leading causes of retraction across the 2010–2019 period. These are not fringe cases. They span institutions, disciplines, and career stages.
What Are Editors Looking for During Image Screening?
Understanding what triggers a desk rejection helps researchers know what to check before submitting. The table below summarizes the main categories of image problems that editorial screening tools flag.
| Image Issue Type | What It Involves | Common Causes |
|---|---|---|
| Duplication or reuse | Identical or near-identical image regions appearing in different panels or figures, including scaled, rotated, or flipped copies | Accidental copy-paste during figure assembly, reuse of control images, collaborator file mix-ups |
| Manipulation | Cloning, splicing, selective erasure, or brightness/contrast adjustments that obscure or alter data | Over-processing during figure preparation, content-aware edits applied without realizing the effect on data |
| AI-generated images | Synthetic images produced by generative AI tools and presented as real experimental data | Increasingly flagged as AI image generators become more accessible; Nature has reported on the growing threat of AI-generated figures in scientific manuscripts |
| Image plagiarism | Reuse of images from previously published manuscripts without attribution | Unintentional reuse from shared lab archives, failure to track image provenance across projects |
| Self-plagiarism | Reuse of a researcher’s own previously published images in a new manuscript | Recycling control images or representative micrographs across studies without disclosure |
Journals that use automated screening tools check for all of these categories simultaneously. Researchers who only verify their text for plagiarism but skip image checks are leaving a significant vulnerability unaddressed.
Why Even Careful Researchers Are at Risk
Many image integrity issues are not the result of deliberate misconduct. They arise during figure preparation, file compression, collaborator handoffs, or accidental reuse of images across manuscripts. A principal investigator may not have direct visibility into every figure prepared by a student or collaborator. A peer-reviewed study at the University of Split found that researchers correctly identified only 32.4% of image duplications when reviewing figures by eye, demonstrating the limits of unaided human review.
Even researchers who uphold the highest ethical standards may unknowingly submit problematic images because of steps handled by others in the workflow. Pre-submission screening is a protective step, not an admission of wrongdoing. It is the visual equivalent of running a spell check: a routine quality assurance measure that catches problems before they become consequential.
What Does a Pre-Submission Image Screening Workflow Look Like?
A practical pre-submission screening workflow takes minutes and follows a straightforward process:
- Upload your manuscript. Submit the full PDF or individual image files to an image integrity tool. Proofig AI accepts both formats and extracts all figures automatically.
- Automated scan. The tool analyzes every figure for duplication, manipulation, AI-generated content, and image plagiarism by comparing against a large corpus of published scientific images. This step runs without manual intervention.
- Review flagged items. The tool surfaces suspected issues with forensic detail, highlighting the specific regions and the type of concern. Not every flag is a real problem. Some are artifacts of image compression, legitimate figure reuse with proper labeling, or tool sensitivity. Proofig AI is validated on large real-world datasets and is designed to minimize false positives, so researchers are not buried in noise when reviewing results.
- Correct and export. Fix genuine issues, consult co-authors where relevant, and export a screening report for your records or for co-author review. This report can serve as documentation that image integrity was verified before submission.
The key principle at every step is that the tool surfaces suspected issues for human review. It does not make final determinations about intent or wrongdoing. The researcher, with full context about their data, makes the final call.
How Pre-Submission Screening Protects the Researcher
The value of catching an image issue before submission is not primarily about satisfying the journal. It is about protecting the researcher. Many of these issues are introduced unintentionally during complex workflows or by collaborators and students, so screening helps ethical researchers validate that no preventable errors slipped through. Post-submission discovery of image problems, whether at desk review, peer review, or after publication, carries progressively higher costs.
A desk rejection delays your work. A post-publication correction becomes part of the permanent record. A retraction carries significant financial and reputational consequences that can affect careers, lab funding, and institutional standing. Nature has reported on the intense personal and career stress researchers face when retracting papers, even when the cause is honest error. More than 8,000 highly cited scientists have at least one retracted publication, demonstrating that this risk spans all career levels.
Catching and correcting an issue before submission means it never becomes a public record. The researcher maintains control.
Treat Image QA as a Standard Part of Manuscript Preparation
Pre-submission image screening is a straightforward step that belongs in every researcher’s workflow, alongside reference verification and text review. The journals you are submitting to are already screening your figures. Running the same check yourself, before you submit, means you arrive at the editor’s desk with confidence that your images are clean.
Review your figures with an image integrity screening tool. Correct what needs correcting. Document the check. Protect your work, your name, and your lab.
