Month: June 2026

Choose Your Image Integrity Software: How Imagetwin Compares to the Alternatives

Posted on by Sofiia

Scientific publishing faces a problem of image manipulation and duplication in research, sometimes accidental, which passes through peer review at a rate that damages the credibility of journals, institutions, and researchers. The good news: automated detection tools have matured significantly. The question is which one fits your workflow.

We broke down the image integrity software landscape, and compared Imagetwin vs Proofig, ReviewerZero, Imacheck, and FigCheck, so you can make an informed decision.

The tools in this space

There are currently a handful of automated image integrity tools available to publishers and institutions: Imagetwin, Proofig, ReviewerZero, Imacheck, and FigCheck. They are not interchangeable. They differ in database size, detection scope, pricing structure, speed, and who they are actually built for.

Proofig AI is an image integrity company based in Israel, its customer base includes universities and research centers, and its pricing model reflects that. ReviewerZero extends beyond image analysis into statistics, citations, and author verification, a broader scope, but less specialized on the image side. Imacheck and FigCheck are narrower tools suited to ad hoc use, without the database depth or publisher integrations that production workflows require.

Imagetwin is built specifically for publishers’ and institutions’ workflows at scale. It is trusted by 8 of the 10 largest academic publishers in the world, including Elsevier, Springer Nature, Wiley, Karger, Sage, Taylor & Francis, and AAAS. Its findings have been cited in ~ 4200 of corrections and retractions on PubPeer, compared to around 70 cases reported for other tools in the space.

Where the real differences are

Speed

Imagetwin returns results in 5 to 30 seconds per manuscript. Most alternatives take 2 to 10 minutes. At submission volume, that gap compounds quickly.

Database 

While other tools’ databases are limited to PubMed, Imagetwin checks against 160M+ published figures drawn from open-access and publisher sources, giving it the broadest cross-publisher detection coverage in the market.

AI-generated image detection

Imagetwin detects AI-generated figures across all life science image types and, uniquely, identifies which generator most likely produced the image: Firefly, DALL-E, Stable Diffusion, ChatGPT, and others. No other tool in this space currently offers generator attribution.

Private repository

Imagetwin includes a free private repository on every plan, including single scans. The equivalent feature from our competitors is available only on enterprise-tier licenses.

File format support

Imagetwin accepts .jpeg, .png, .gif, .jfif, .bmp, .tif, .tiff, .svg, and .webp, plus direct upload of .doc and .docx manuscripts. The competitors’ is limited to PNG and JPEG only.

Who should use what

Choose Imagetwin if you are a publisher, journal editor, institution, or production partner running integrity checks at submission volume. It is built for that workflow: fast, scalable, deeply integrated with the major submission systems, and priced accordingly.

ReviewerZero is worth evaluating if you need integrity checks that go beyond images, statistics, citations, and author identity, though it is less specialized on the image side.

FigCheck and Imacheck are low-cost options for ad hoc use, but neither offers the database depth, AI detection, or publisher integrations that production workflows require.

Feature Imagetwin Other Image Integrity Tools
Performance
Speed 5–30 seconds per manuscript 2–10 minutes per manuscript
Pricing model Per paper, fixed cost regardless of figure count, enterprise plans tailored to your organization Per sub-image, cost scales with every figure
Browser support All browsers Chrome, other browsers not recommended
Bulk upload Yes Not available in certain competitors’ tools
Database & Plagiarism Detection
Database size 160M+ figures (open-access + publisher sources) PubMed Central database only
Duplicate detection depth Identifies smallest partial duplications in the database comparison Identifies reuse of entire (sub) figures
Image Type Support
Supported image file formats .jpeg, .png, .gif, .jfif, .bmp, .tif, .tiff, .svg, .webp .png, .jpeg only
Document upload (.doc/.docx) Yes No
Image types supported All life science image types (microscopy, western blots, FACS, graphs, charts, spectra, illustrations) No detailed analysis for spectra or illustrations
Spectra Yes No
Illustrations and diagrams Yes Unknown
Manipulation Detection
Confidence scores AI-based assessment with a detailed score between 0%–100% per finding Simpler matched keypoints or other metrics
Manipulation image types supported Microscopy, western blots, FACS, graphs, charts, spectra, illustrations Varies between competitors, not supported by most tools
AI-Generated Image Detection
AI image detection All life science image types (microscopy, histology, western blots, cell cultures, spot images) No AI detection or limited image types
Generator identification Yes, identifies most probable generator (Firefly, DALL·E, Stable Diffusion, ChatGPT, etc.) Identifies AI vs. not AI only, no attribution
C2PA metadata verification Yes Not available
Workflow & Integrations
Bulk upload Yes Not available
Private repository 1 free per user; shared repositories on organization yearly plans; free on all plans, including single scan Enterprise-tier only
Verified findings on PubPeer Above 4,000 corrections and retractions (~4,200) 60–70 cases reported per competitor
Publisher partnerships Wiley, Karger, Sage, Taylor & Francis, IOP, Elsevier, Springer Nature, AAAS, 8 of 10 largest publishers Primarily institution-facing

Frequently asked questions

Imagetwin uses machine learning models trained on life science image types to flag AI-generated figures. It goes a step further than binary detection by identifying the most probable generator – Firefly, DALL-E, Stable Diffusion, ChatGPT, and others – with a confidence score per finding.

The main differences are pricing model (per paper vs per sub-image), database breadth (160M+ figures vs PubMed only), speed (seconds vs minutes), and AI detection scope. Imagetwin also offers generator attribution and a free private repository on all plans.

Case Study: How ASM Strengthens Image Integrity Screening with Imagetwin

Posted on by Sofiia

How ASM Uses Imagetwin

ASM is one of the largest life science societies and a nonprofit publisher of 17 scientific journals. Maintaining trust in published research is central to its mission. To strengthen its integrity screening process, ASM integrated Imagetwin into its editorial workflow in 2023.

Since then, ASM has processed nearly 13,000 scans through Imagetwin, including both direct usage and additional screening via an external service provider.

Imagetwin is primarily used by ASM’s Ethics and Integrity team, where where scientifically trained team members conduct image screening as part of routine editorial checks. The team uses the platform in three main ways.

  1. Routine prescreening of figures

All manuscripts containing halftone images (microscopy, gels, immunofluorescence) are screened for potential image duplication during the revision or resubmission stage – the same stage where other ethics checks, such as text similarity, are performed.

The Ethics and Integrity team reviews the Imagetwin report and evaluates flagged images. Flagged images are first manually reviewed by a specialist using expert visual inspection, then verified using Adobe Photoshop’s Difference function to reduce false positives.

  1. Verification of corrected figures

When authors revise figures after duplication concerns have been raised, Imagetwin is used again to verify that previously identified issues have been resolved, and no new duplications have been introduced. The second check ensures that corrections fully address all concerns.

  1. Assessment of post-publication image concerns

When concerns about potential image duplication in published articles are raised by readers or other third parties, Imagetwin is used to assess the reported issue and conduct a broader review of the article’s figures. In addition to validating the specific concern, the scan helps identify any additional image duplications that may not have been initially reported. This comprehensive review enables the Ethics and Integrity team to address all image-related concerns at once, helping ensure that any necessary corrections are complete and minimizing the need for multiple post-publication corrective measures.

Key Numbers From The ASM Pilot

During a one-year pilot (March 2023 – March 2024), ASM screened 2,627 manuscripts from 15 journals at the post-acceptance stage. The pilot launched initially with Microbiology Spectrum for one month before expanding to all ASM journals.

Key findings published:

  • 410 out of 2,627 screened manuscripts (~15.6%) showed image-related concerns overall
  • 9% of accepted manuscripts (248 out of 6,416) that had already passed peer review contained image duplication issues detected before publication
  • Image duplication accounted for approximately 60% of all figure-related concerns
  • Resolving issues pre-publication required ~1.5 hours on average, compared to up to 10 hours post-publication
  • In 50% of editorial consults (6 manuscripts), acceptance was revoked due to insufficient author response or lack of original data

Most Valuable Capabilities

According to the ASM team, the most valuable functionality has been duplication detection and cross-publication image reuse detection. Imagetwin can identify when images from previously published papers appear in a new submission, allowing editorial teams to review whether the reuse is legitimate or problematic.

Why ASM Made Image Checks a Standard Part of Review

Image duplication in scientific publications can occur either intentionally or unintentionally. Regardless of the cause, it raises serious concerns.

Duplicated figures can:

  • undermine confidence in the reported findings
  • raise questions about scientific rigor and research practices
  • potentially suggest research misconduct
  • damage the credibility of peer review and editorial oversight
"When images are duplicated in scientific publications, whether by mistake or design, it compromises confidence in the research and those behind it... Ultimately, these practices weaken trust not only in the study itself but across the broader scientific enterprise." — ASM Ethics and Integrity Team

Since 2023, Imagetwin has become an integral part of ASM’s ethics screening workflow across its portfolio of journals. For society members and affiliated journals, this means stronger protection of the scientific record, no risk of reputational harm, transparency in editorial processes, and full trust in the peer review system.

Frequently asked questions

Imagetwin is built specifically for publishers and integrates directly into editorial systems like ScholarOne and Editorial Manager, so screening happens inside the existing workflow without extra steps. Pricing is per paper rather than per sub-image, making costs predictable at scale. ASM processed nearly 13,000 scans through Imagetwin, the kind of volume that makes per-sub-image pricing prohibitive.

Yes, and the ASM data makes this concrete. During their pilot, around 15% of manuscripts that had already passed peer review were found to image related issues before publication. Peer reviewers assess scientific merit, they are not trained to detect pixel-level manipulation or cross-publication image reuse, which requires automated comparison against a large image database.

Imagetwin screens halftone figures including microscopy, gels, immunofluorescence, Western blots, FACS plots, and XRD spectra, as well as detecting cross-publication image reuse. It also identifies AI-generated figures. ASM uses it specifically for manuscripts containing halftone images, which are the figure types most commonly associated with duplication concerns in life science research.