CASRAI Dictionary

Tag: image manipulation

  • Image integrity and manipulation detection in research publishing

    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.

  • Disclosing AI-generated images and figures in research

    Most of the debate about generative AI in research has concerned the written word: what authors must declare when they use AI tools to help draft a manuscript. But generative AI does not only produce text. It produces images — and images in a scientific paper occupy a fundamentally different place from prose. A figure is often presented as evidence: a micrograph, a gel, a scan, a chart of results is taken to be a faithful record of something that was observed or measured. When such an image can be conjured by a model that has never observed anything, the most basic assumption of scientific communication — that what you are shown is real — comes under threat. This is why AI-generated images raise concerns that are sharper, and in some respects more dangerous, than those raised by AI-generated text. The question belongs within the generative-AI disclosure domain of the CASRAI Dictionary.

    Why images are different from text

    The crucial difference is the relationship between an image and reality. Text in a paper is understood to be the authors’ account, their argument and interpretation; readers know it is written. But many images in scientific papers are understood to be records of fact — this is what the cells looked like, this is the structure that was resolved, this is the experimental output. The integrity of the entire scientific record depends on that understanding holding true. A generative model can now produce an image that looks exactly like such a record but corresponds to nothing that was ever observed. In the context of a results figure, that is not illustration; it is fabrication, which has always been among the gravest forms of research misconduct. The danger is not hypothetical or cosmetic: a fabricated figure can mislead readers, reviewers and an entire field into believing in findings that do not exist.

    The integrity and manipulation concerns

    Several specific concerns flow from this:

    • Fabrication of results. The starkest risk is the generation of fake data visualisations, fake imaging results or fake experimental outputs presented as genuine — falsification of the scientific record itself.
    • Undisclosed manipulation. AI tools that retouch, enhance or alter genuine images can cross the long-standing line between acceptable adjustment and impermissible manipulation, especially when done invisibly.
    • Erosion of trust. If readers can no longer assume that scientific images are authentic, the evidential value of figures — central to how science is communicated and checked — is undermined across the board.
    • Detection difficulty. Synthetic images can be very hard to distinguish from real ones, which means the safeguards cannot rely on catching fakes after the fact and must lean heavily on honesty and clear rules.

    These concerns sit on top of a pre-existing problem: image integrity, including inappropriate duplication and manipulation of figures, was already one of the most common sources of research-integrity cases before generative AI arrived. The new tools pour fuel on a fire already burning.

    Why many publishers ban AI-generated figures

    Faced with this, a notable number of publishers and journals have taken a stricter line on images than on text. Where policies on AI-assisted writing typically permit it subject to disclosure, policies on AI-generated images frequently prohibit them in scientific figures outright, particularly where a figure represents data or results. The reasoning follows directly from the difference described above. An AI tool that helps phrase a sentence does not pretend the sentence is an observation; an AI-generated results figure, by its nature, presents as real something that is not. Because the risk is fabrication of evidence rather than mere stylistic assistance, the proportionate response is often a ban rather than disclosure. A common pattern in publisher policy is therefore a near-prohibition on generative AI for figures that depict data, alongside more permissive, disclosure-based treatment of AI used for purely decorative or schematic illustrations that no one would mistake for evidence — and even then, transparency is expected.

    The disclosure requirement

    Where AI-generated or AI-assisted imagery is permitted at all — for example, a clearly labelled conceptual illustration, a graphical abstract, or a schematic — disclosure becomes the governing requirement, as it is for text. The emerging expectations include several elements:

    • Declare any use. Authors should state where and how generative AI was used to create or alter images, so that readers and editors are not misled about what they are looking at.
    • Never pass off generated content as real data. The bright line is that no AI-generated image may be presented as an authentic record of an observation or result.
    • Preserve and provide originals. Authors are increasingly expected to retain unaltered original images and supply them on request, so that genuine figures can be verified.
    • Follow the specific journal’s policy. Because rules differ between venues, authors must check and comply with the policy of the journal they are submitting to.

    COPE and the integrity bodies

    Editorial and integrity organisations have shaped this developing consensus. The Committee on Publication Ethics (COPE) and publishers’ own guidance consistently anchor the discussion in long-standing principles of image integrity — that figures must faithfully represent the work, that manipulation which misleads is misconduct, and that AI does not change these duties but makes them more pressing. The framing is instructive: generative AI has not created a new category of wrongdoing so much as made an old and serious one — fabricating or manipulating the visual evidence in a paper — far easier to commit and harder to detect. The policy response is a reassertion of the principle that scientific images must be honest, adapted to a world in which dishonest images are trivially easy to make. These questions connect closely to the responsibilities discussed in our material on authorship and accountability.

    A consistent vocabulary for disclosure

    For disclosure of AI use in images to be meaningful across journals, repositories and integrity systems, what is disclosed must be described consistently — what tool was used, on which figures, for what purpose, and whether the result is illustrative or evidential. That consistency is what the CASRAI Dictionary works towards: a shared vocabulary so that a statement about AI use in a paper’s figures is understood the same way wherever it is recorded. And because creating genuine figures and visualisations is real research contribution, that work can be described using the same framework as any other — the CRediT taxonomy and its full set of contribution roles, with Visualization recognising the creation of legitimate data presentation. Generative AI can make a convincing picture of anything; the durable principle — that the images in a scientific paper must be truthful representations of real work — is exactly what disclosure and these firmer rules exist to defend.