CASRAI Dictionary

Tag: image manipulation research misconduct

  • Elsevier’s Research Integrity Screening Process

    Elsevier screens research submissions for integrity issues through a layered pipeline: automated tools such as Check Integrity and Crossref Similarity Check flag plagiarism, duplicate submissions and image anomalies at intake, specialist Research Integrity and Publishing Ethics (RIPE) analysts investigate confirmed concerns, and outcomes range from correction through expression of concern to full retraction, following guidelines set by the Committee on Publication Ethics (COPE).

    Research integrity screening is the set of technical checks and human review stages a publisher applies to a manuscript, before and after publication, to detect fabrication, falsification, plagiarism, undisclosed image manipulation and paper-mill activity. At Elsevier, that pipeline runs continuously from the moment a manuscript is submitted to the point, if necessary, of retraction.

    How Elsevier’s research-integrity pipeline works, from submission to retraction

    Elsevier operates one of the largest editorial screening operations in scholarly publishing. In 2025, the publisher received 4.2 million manuscript submissions across roughly 3,000 journals and published 795,000 after validation and peer review, according to Elsevier’s own account of its editorial process. Elsevier states that its published output accounts for over 18% of global research output and 29% of citations — a scale that shapes why it has invested heavily in both automated screening and dedicated integrity staff rather than relying on peer review alone.

    The pipeline runs across four broad stages, each with a different primary tool or team responsible for catching a different class of problem.

    Stage Primary tool or team Typical trigger
    Submission intake Check Integrity screening tool; Crossref Similarity Check (iThenticate) Text overlap, duplicate manuscript, unauthorised authorship change
    Peer review Editors, external reviewers, RIPE analysts Implausible data, reviewer-flagged inconsistency, suspicious image reuse
    Post-publication monitoring Research Integrity and Publishing Ethics (RIPE) team Reader or whistleblower reports, cross-journal pattern analysis
    Enforcement Editors-in-chief, following COPE-guided process Confirmed fabrication, falsification or plagiarism

    What does Elsevier screen for at the point of submission?

    Every manuscript submitted to an Elsevier journal is routed through automated checks before an editor sees it. Check Integrity, Elsevier’s proprietary screening tool, had been expanded across more than 2,000 journals as of March 2026, according to trade press coverage in Research Information. The tool automatically reviews submissions for red flags — including unauthorised authorship changes, undisclosed conflicts of interest and signs of duplicate or template-like submission — and routes anything flagged to specialist integrity analysts, freeing editors to focus on scientific merit.

    Plagiarism screening runs in parallel through Crossref Similarity Check, powered by iThenticate, which compares submitted text against a large index of published articles and web content. There is no fixed similarity percentage that automatically triggers rejection; editors interpret each report to distinguish appropriate citation from genuine textual misconduct.

    Paper-mill detection layers on top of these checks. Integrity analysts look for patterns that recur across industrialised fraud, including:

    • Formulaic, template-like titles or methods sections
    • Unusual or inconsistent author affiliations and contact details
    • Data or experimental descriptions that do not match the stated methodology
    • Systematic image reuse across ostensibly unrelated papers
    • Irregular peer-review patterns, such as reviewer suggestions tied to the same small pool of contacts

    How does Elsevier detect image manipulation and data-integrity problems?

    Image screening combines editorial guidelines with a mix of manual and software-assisted checks. Elsevier’s policy permits minor adjustments to brightness, contrast or colour balance only where they do not obscure or eliminate information present in the original image; the use of generative AI to create or alter a figure is prohibited outright. Where manipulation is suspected, editors can apply forensic image-analysis tools of the kind recommended by the US Office of Research Integrity, and will typically request the original, unprocessed image files directly from the authors.

    Elsevier has also published on the scale of automated flagging behind these checks. At the 8th World Conference on Research Integrity in 2024, Elsevier data scientist Yuri Kashnitsky presented on large-scale flagging of integrity misconduct across the publisher’s portfolio, noting that all system-generated findings are manually checked and confirmed by investigators before any corrective action is suggested to editors — underscoring that software narrows the search space, but a human analyst still makes the determination.

    Who investigates confirmed misconduct, and what enforcement follows?

    Once a concern is substantiated, Elsevier’s in-house Research Integrity and Publishing Ethics (RIPE) team leads the investigation, working with journal editors and, where warranted, the authors’ institutions. Elsevier states that it follows retraction guidelines developed by COPE, and confirmed problems resolve into one of three outcomes: a correction or erratum for errors that do not undermine the paper’s conclusions, an expression of concern where the investigation is inconclusive but doubts remain, or a retraction where the findings are no longer considered reliable.

    A recent case shows this enforcement ladder operating at scale. In a statement updated in May 2026, Elsevier disclosed that a comprehensive, multi-year audit of the journal Heliyon — using Check Integrity screening combined with manual review by RIPE analysts — had produced approximately 1,100 corrections to the scientific record, affecting around 3% of everything the journal had published across 12 years. Those 1,100 actions spanned corrections, expressions of concern and retractions; impacted authors were notified and given the chance to respond before editors made a final determination. Following the audit, Web of Science removed an indexing hold it had placed on Heliyon, and Elsevier said it was applying lessons from the case to workflows across its wider journal portfolio.

    Common questions about Elsevier’s integrity screening

    Does Elsevier use iThenticate for plagiarism screening?

    Yes. Elsevier’s journals route submitted manuscripts through Crossref Similarity Check, which is powered by iThenticate, comparing text against a large index of published articles and web content. Editors, not the software alone, judge whether flagged overlap reflects proper citation or genuine plagiarism before any editorial decision is made.

    Who investigates allegations of research misconduct at Elsevier?

    Elsevier’s in-house Research Integrity and Publishing Ethics (RIPE) team investigates confirmed concerns, working alongside journal editors and, where relevant, the authors’ institutions. Investigations follow COPE guidelines and typically involve requesting raw underlying data before any corrective action is taken.

    What is considered the most serious form of research misconduct?

    Fabrication and falsification of data are generally treated as the most serious forms of misconduct, alongside plagiarism, because they directly corrupt the reliability of the published record. Elsevier’s policies place these above lesser breaches such as citation gaming or unresolved authorship disputes.

    What happens after a research-integrity investigation confirms a problem?

    Confirmed issues lead to one of three outcomes: a correction for errors that do not undermine the findings, an expression of concern where evidence is inconclusive, or a retraction where the results are no longer considered reliable. All three are published and linked to the original article, per COPE guidance.

    What this means for institutions, authors and integrity offices

    For research administrators, the Heliyon case is a reminder that publisher-side screening is a complement to institutional processes, not a substitute for them. When a journal’s RIPE team contacts an institution about a flagged submission or published paper, that request typically triggers — and depends on — the institution’s own research-integrity office and record-keeping, an area covered in more detail in CASRAI’s research administration resources and its wider research-integrity dictionary entries. Authors, in turn, should expect to be asked for raw, unprocessed data or images at any stage, including years after publication, and should retain those records accordingly.

    Elsevier is not acting alone: it collaborates with other publishers through the STM Integrity Hub to detect duplicate submissions across the wider industry, reflecting a broader shift toward cross-publisher, not just single-journal, integrity infrastructure. As automated screening tools mature, the balance is likely to keep shifting toward earlier detection at submission — but the Heliyon audit shows that human RIPE analysts, not algorithms, remain the ones who make the final call on correction, expression of concern or retraction.

  • Recent History of Research Misconduct Scrutiny

    The recent history of attention to research misconduct runs from the 1986 Baltimore Case — which forced the creation of the US Office of Research Integrity (ORI) — through the 2000s rise of paper mills and image manipulation, to today’s concern that generative AI can fabricate entire papers, datasets, and images at a scale no journal can screen manually. Each phase added new oversight infrastructure without resolving the underlying incentive to publish at any cost.

    Research misconduct is defined by US federal policy as fabrication, falsification, or plagiarism (FFP) in proposing, performing, or reviewing research, or in reporting research results. This article traces how public and institutional attention to that problem has shifted — from a single congressional hearing in 1981 to a global infrastructure of retraction databases, publication-ethics bodies, and AI-detection tooling.

    The Baltimore Case: the event that built America’s oversight system

    The single most consequential episode in this history began in 1986, when postdoctoral researcher Margot O’Toole challenged the validity of data in an immunology paper published in Cell, co-authored by Nobel laureate David Baltimore and led by Thereza Imanishi-Kari. What started as a laboratory dispute became a decade-long federal investigation involving the NIH, congressional hearings chaired by Representative John Dingell, and forensic analysis of lab notebooks by the US Secret Service.

    Baltimore was never accused of fraud, but his public defence of Imanishi-Kari drew sustained criticism and cost him the presidency of Rockefeller University in 1991. Imanishi-Kari was formally cleared in 1996, when an appeals panel found her records sloppy but the evidence of intentional fabrication insufficient. By then the case had already reshaped US science policy.

    Congressional attention actually predates the Baltimore Case: Representative Albert Gore Jr held the first hearing on research misconduct in 1981, after roughly twelve cases surfaced at major US research centres between 1974 and 1981. It was the Baltimore Case’s decade of scrutiny, though, that cemented the need for a permanent federal body rather than ad hoc congressional inquiries.

    From ORI to COPE: institutionalising oversight (1992-2005)

    The US Office of Research Integrity (ORI) was formally established within the Department of Health and Human Services in May 1992, consolidating two predecessor offices created in 1989. The NIH Revitalization Act of 1993 made ORI independent and replaced “scientific misconduct” with “research misconduct” in federal policy, widening the definition beyond laboratory science.

    The Federal Research Misconduct Policy, published in the Federal Register on 6 December 2000, gave the US its first government-wide fabrication/falsification/plagiarism definition, still the reference ORI applies today. Internationally, the UK created the UK Research Integrity Office (UKRIO) in 2006, and the Committee on Publication Ethics (COPE) was founded by medical journal editors in 1997, giving publishers — not just funders — a formal adjudication mechanism.

    This period set the template still in use: institutions investigate first, an oversight body reviews the finding, and journals retract independently of any funder decision.

    Paper mills and image manipulation: the digital-era escalation

    Digital publishing made two categories of misconduct systemically visible. First, image manipulation: a 2016 screening study by microbiologist Elisabeth Bik and colleagues in mBio examined 20,621 papers across 40 journals and found 3.8% contained inappropriate image duplication, roughly half apparently deliberate. The 2005-2006 exposure of South Korean stem-cell researcher Woo Suk Hwang’s fabricated cloning results, which relied partly on manipulated photographs, pushed journals toward routine image screening for the first time.

    Second, paper mills: for-profit operations that manufacture fraudulent manuscripts, often with fabricated data or templated “tortured phrase” plagiarism, sold to researchers under career pressure to publish. Documented paper-mill output dates to the 2000s but accelerated sharply through the 2010s.

    The consequence is visible in the retraction record. 2023 was, per Nature’s coverage of Crossref and Retraction Watch data, a record year with over 10,000 retractions globally, a large share traced to paper-mill activity at a small number of publishers, notably Hindawi. That same year the Retraction Watch Database — previously subscription-only — was made freely available after Crossref took over its stewardship.

    Milestones in the recent history of attention to research misconduct
    Year Event Significance
    1981 First congressional hearing (Rep. Albert Gore Jr) Research misconduct becomes a public policy issue in the US
    1986-1996 Baltimore Case investigation and appeal Exposes inadequacy of ad hoc federal response; drives creation of ORI
    1992 Office of Research Integrity (ORI) established First permanent federal body dedicated to research misconduct
    1997 Committee on Publication Ethics (COPE) founded Gives journals a shared ethics framework independent of funders
    2000 Federal Research Misconduct Policy published Standardises the fabrication/falsification/plagiarism (FFP) definition
    2005-2006 Hwang Woo-suk stem-cell fraud exposed Establishes routine image-manipulation screening at journals
    2010s Paper mills scale up Fabricated manuscripts and fake peer review sold commercially
    2023 Record 10,000+ retractions; Retraction Watch Database opened via Crossref Retraction data becomes a shared, searchable public resource
    2023-present Generative AI text and image fabrication concerns Detection tools race to keep pace with synthetic fabrication at scale

    AI-era fabrication: what has genuinely changed

    Generative AI has not created a new category of misconduct — fabrication, falsification, and plagiarism remain the operative definitions — but it changes the economics of producing it. Large language models draft plausible manuscript text at near-zero marginal cost, and image generators can produce synthetic western blots or microscopy images that mimic genuine experimental output. Paper mills, already operating at scale before 2023, are widely reported to be early adopters of these tools.

    What is different is detection asymmetry: AI-generated text is often hard to distinguish from human writing using plagiarism tools built to match against a corpus of prior text, not to catch novel synthetic prose. Journals are responding with statistical-anomaly detection and image-forensics tooling, but this is explicitly reactive — the same pattern seen after the Baltimore Case and the Hwang case, where scrutiny follows scandal rather than anticipating it. The publish-or-perish incentive that produced the Baltimore Case in 1986 is the same incentive AI-assisted fabrication now threatens to industrialise further.

    Common questions on the history of research misconduct

    What are some examples of research misconduct?

    Under the US Federal Research Misconduct Policy, examples fall into three categories: fabrication (inventing data or results), falsification (manipulating research materials, equipment, or processes, or altering/omitting data), and plagiarism (appropriating another’s ideas, processes, results, or words without credit). Honest error and differences of scientific opinion are explicitly excluded.

    What group formed in 1992 to investigate scientific misconduct?

    The Office of Research Integrity (ORI) was established in May 1992 within the US Department of Health and Human Services, consolidating two predecessor offices — the Office of Scientific Integrity and the Office of Scientific Integrity Review — created in 1989. ORI remains the federal oversight body for misconduct in Public Health Service-funded research.

    What are the effects of research misconduct in today’s society?

    Research misconduct erodes trust between researchers, institutions, and funders, and it can distort the evidence base that clinical guidelines, policy decisions, and further research rely on. High-profile cases also fuel broader public scepticism about science, which institutions such as COPE and ORI argue makes rigorous, transparent investigation processes essential rather than optional.

    Implications for institutions, publishers, and funders

    For research administrators, the pattern is instructive: every major expansion of oversight infrastructure — ORI in 1992, COPE in 1997, routine image screening after 2006, the open Retraction Watch Database in 2023 — followed a scandal rather than preceding one. Institutions waiting for their own “Baltimore Case” before investing in integrity training and screening tools are, by this history, already behind.

    • Establish clear, documented processes for handling allegations before one arises, mirroring the institutional-first-response model ORI has required since 1989.
    • Adopt image-integrity and plagiarism screening as a routine pre-submission and pre-award step, not a post-publication response.
    • Track Retraction Watch Database entries relevant to your institution’s output as a standing due-diligence practice, now that the database is freely accessible via Crossref.
    • Treat AI-generated text and image detection as an evolving capability requiring periodic reassessment, not a one-off procurement decision.

    The throughline from 1986 to today is not that misconduct has become more common, but that the tools for producing it — and, gradually, for detecting it — have industrialised in step with the technology available in each era. The next inflection point in this history will likely be defined by whether detection capability can keep pace with generative AI, or whether institutional attention once again waits for the next public scandal to force the issue.