In much of the life sciences, the image is the evidence. A western blot, a micrograph, a gel, a fluorescence panel — these figures are not illustrations of a result; they are the result, the primary data on which a paper’s claims stand or fall. That centrality is exactly what makes image integrity such a serious matter. A figure that has been improperly altered — a band duplicated to suggest a result that was not obtained, two images spliced together as if they were one, the same micrograph reused to represent two different experiments — can make a false claim look like solid evidence. Image problems have driven a substantial share of corrections and retractions, and detecting them is now a recognised part of safeguarding the literature. This article examines image integrity and its detection, drawing on the research integrity domain of the CASRAI Dictionary.
What image problems look like
Image integrity issues span a spectrum from honest error to deliberate fabrication, and a responsible approach must keep that spectrum in view. Common categories include:
- Duplication. The same image, or a portion of it, appears more than once — representing different samples, conditions or experiments — whether by mistake or by design.
- Manipulation. An image has been altered in ways that misrepresent the underlying data: bands erased or added, contrast adjusted to hide or create features, elements cloned or removed.
- Splicing. Separate images, or non-adjacent lanes of a gel, are combined and presented as a single continuous image without disclosure.
- Reuse. An image from an earlier paper is reused to stand for a different result, sometimes rotated, cropped or rescaled to disguise the reuse.
Some of these arise from sloppiness, mislabelling or a poor understanding of acceptable figure preparation; others are deliberate misconduct. Distinguishing the two is a matter for careful, fair investigation, but the first step is simply detecting that something is amiss.
Screening tools and forensic detection
For a long time, image problems were caught only when a sharp-eyed reader, editor or reviewer happened to notice them — an unreliable safety net given the volume of figures published. The development of forensic image-screening tools has changed this. Software designed to detect image manipulation and duplication — with tools such as Proofig and ImageTwin among the better known — can scan a manuscript’s figures and flag suspicious features: regions that appear duplicated within or between images, signs of cloning or splicing, and matches against other published images. These tools do not pronounce guilt; they surface candidates for human examination, dramatically increasing the chance that a problem is caught before publication rather than after. The expert work of interpreting a flag — deciding whether it reflects an innocent explanation, a correctable error or genuine misconduct — remains firmly with people, but the tools make systematic screening feasible at scale.
Bringing screening into the workflow
The most important shift is the move to screen images before publication, as part of the editorial workflow, rather than relying on post-publication discovery. A growing number of journals and publishers now incorporate image screening into their processes — running figures through forensic tools at submission or before acceptance, so that potential problems can be raised with authors and resolved while the paper is still under consideration. This is far preferable to discovering an image problem after publication, which can mean correction, expression of concern or retraction, with all the disruption and reputational cost that entails. Pre-publication screening is becoming a standard quality-control step in the same way that plagiarism screening did before it — a routine part of preparing the scholarly record rather than an extraordinary intervention.
The role of COPE and integrity bodies
Detecting a possible image problem is only the beginning; what happens next must be fair, consistent and proportionate, and this is where guidance from integrity bodies is essential. The Committee on Publication Ethics (COPE) provides editors with guidance and flowcharts for handling suspected image manipulation and related concerns — how to raise the issue with authors, how to involve institutions, how to distinguish error from misconduct, and how to apply remedies such as correction or retraction appropriately. This guidance matters because an image flag is an allegation with serious consequences for the people involved, and due process is non-negotiable. In some jurisdictions, formal oversight bodies are also involved: in the United States, the Office of Research Integrity (ORI) oversees integrity in federally funded research and has long dealt with image-based allegations as part of misconduct cases. Together, these bodies ensure that the response to a detected problem is governed by recognised norms rather than improvised.
Prevention as well as detection
Detection is necessary but not sufficient; preventing problems is better. Much can be achieved through clear standards for figure preparation — what adjustments are acceptable, what must be disclosed, how gels and blots should be presented — and through education, so that researchers understand where the line lies before they cross it inadvertently. Requiring that the original, unprocessed image data be available for checking is another powerful deterrent and aid to resolution. Image integrity, in other words, is part of the broader culture of responsible conduct: it is supported by good training, transparent data practices and clear expectations, not by screening tools alone. The wider context of integrity practice and authorship responsibility is explored across our authorship resources.
A consistent vocabulary for integrity
For image-integrity concerns to be handled consistently across journals, publishers and institutions, the concepts involved must be described the same way everywhere — what constitutes manipulation, what the categories of concern are, and how outcomes such as corrections and retractions are recorded. That consistency is what the CASRAI Dictionary provides: a shared vocabulary so that integrity information travels accurately wherever it is recorded. And because honest figures rest on honest contribution, the work behind every paper can be described in the same framework used throughout the record — the CRediT taxonomy and its full set of contribution roles, including the investigation and data curation on which sound images depend. Figures carry the weight of evidence; protecting their integrity protects the literature itself.