Image Duplication Detection Software

Scientific image duplication or figure reuse can compromise transparency and misrepresent results. Imagetwin helps identify reused, cropped, rotated, or mirrored figures both within and across research papers.

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Trusted Image Integrity Solutions

Purpose-built technology for detecting duplicates and ensuring research transparency.

Accurate Duplication Detection

Flags identical and altered versions of images, including cropped, rotated, or mirrored figures.

Designed for the Scientific Community

Imagetwin is built for publishers, institutions, universities, and researchers to support high-quality screening.

Trained on Real Research Images

Detection is based on figures commonly used in publications, such as microscopy, histology, and western blots.

Image Duplication Detection Software

Reusing figures across multiple submissions or within the same paper, without transparency or attribution, can mislead readers and reviewers. Imagetwin identifies self-plagiarism in research even when they are cropped, rotated, or slightly altered.

The tool is updated regularly to improve detection accuracy and can help screen preprints, accepted papers, or archived content for self-plagiarism concerns.

What Image Duplications can Imagetwin detect?

Imagetwin identifies a broad range of visual duplication types, including subtle and transformed reuse that often escapes manual review. These include:

Exact Duplications

Identical images reused in multiple places, either within the same manuscript or across separate submissions. These may appear in different contexts or figure labels.

  • Detected within the same manuscript or across separate submissions
  • Flagged even when relabeled or repositioned to appear as distinct data
  • Highlighted with bounding boxes and confidence scores

Rotated or Flipped Images

Images that have been rotated or mirrored to obscure reuse. This includes 90, 180, or 270 degree rotation as well as horizontal or vertical flipping.

  • Automatically detects rotations at 90°, 180°, and 270° angles
  • Recognizes both horizontal and vertical mirroring
  • Auto-aligns transformed images for side-by-side comparison

Contrast and Brightness Adjustments

Subtle changes in brightness, exposure, or contrast can be used to make reused figures appear unique. The system detects visual similarity even after tonal manipulation.

  • Automatically detects brightness and exposure variations
  • Identifies contrast modifications that alter appearance
  • Ensures accurate matching even after tonal adjustments

Plagiarised Figures from Published Articles

Images that appear across separate publications without proper attribution. These are detected through comparison with over 150 million indexed figures. See Image Plagiarism.

  • Detects figures reused across published articles
  • Compares against a database of 150M+ indexed images
  • Flags potential plagiarism and missing attribution

Partial or Cloned Regions

Small regions copied from one part of an image into another. These may include gel bands, microscopy sections, or background textures to fill space or fabricate results. 

  • Detects duplicated regions within the same image
  • Identifies copied elements like bands or textures
  • Flags potential manipulation or fabricated content

Cropped or Resized Reuse

Figures that have been cropped, zoomed in, or resized to look visually distinct.

  • Detects cropped or zoomed-in image regions
  • Identifies resized figures that alter appearance
  • Matches partial overlaps even when only a portion of the original figure is reused

How Imagetwin Detects Image Duplication

Imagetwin detects image duplication in three simple steps.

1

Paper or Image Submission

User uploads entire manuscripts, files or individual images to our secure platform for analysis.

2

AI-Powered Analysis

Uses pattern recognition and deep learning to detect duplication, even in altered images.

3

Confidence Score & Reporting

Get a report with flagged issues, similarity overlays, and confidence scores.

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Powerful Features for Duplicate Detection

Everything you need to efficiently manage, prioritize, and track image duplication checks.

AI-Powered Figure Matching

Detects identical, cropped, rotated, flipped, or resized figures across manuscripts and published literature.

Confidence Scores

Assigns a probability score from 0-100% to help assess the severity of detected issues, allowing reviewers to prioritize

Private Repository

Create your own database of articles and compare new submissions with your repository to detect duplicates

PDF Reports

Generate detailed PDF reports showing matched figures, bounding boxes, and source references.

API Access

Integrates into existing workflows, enabling large-scale automated integrity checks through bulk processing and APIs. Ideal for publishers, journals, and institutions.

Filters

Use advanced filtering options to exclude irrelevant detections, sort flagged images, and quickly navigate results

Why Choose Imagetwin?

Image duplication and reuse can undermine research integrity. Imagetwin helps publishers, institutions, and researchers detect these issues early, supporting transparent, high-quality science.

Optimised for Scientific Images

Built specifically to detect duplication in research images such as microscopy, histology, western blots, and charts.

Our detection process is designed to be efficient, helping journals, publishers, and institutions identify concerns early in the publication process.

We prioritize data encryption and privacy, ensuring your files remain confidential.

 

Detects duplication even after cropping, rotating, flipping, resizing, or adjusting contrast and brightness.

Trusted by publishers, editors, and research institutions for pre- and post-publication integrity checks.

 

Detection capabilities are updated regularly based on user feedback, expanded datasets, and new manipulation patterns.

 

Protect Research Integrity with Confidence

Start using Imagetwin to detect image integrity issues and support trustworthy research publishing.

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Frequently asked questions

Imagetwin detects a broad range of duplication types, including exact duplicates, cropped or resized reuse, rotated or flipped images, brightness and contrast adjustments, partial or cloned regions, and plagiarised figures from published articles. Detection works both within a single manuscript and across published literature.

Imagetwin normalises transformed images, ensuring accurate comparisons even when figures have been rotated, flipped, or resized. The auto-align feature allows side-by-side comparison of the original and transformed versions.

Flagged duplicates are highlighted with precise bounding boxes showing the matched areas. Each case includes matched keypoints, similarity overlays, and a confidence score from 0–100% so reviewers can quickly assess severity.

Yes. Imagetwin compares figures against a database of over 150 million published scientific images. Matches are flagged with source citations including DOI, PubMed, and PMC metadata, making it easy to trace the original publication. For more detail, see Image Plagiarism.

Imagetwin is trained on real research figures and detects duplication across all major scientific image types, including microscopy images, western blots, gel electrophoresis, flow cytometry (FACS & FC), histology and pathology slides, scientific photographs, and cell cultures.