What tools and browser extensions can help detect manipulated images or fabricated citations in health articles?

1. The image‑forensics heavyweights publishers are using

Major scholarly publishers have moved from ad‑hoc human checks to commercial AI services that scan figures for duplication, splicing, rotation and other tampering: Science has adopted Proofig across its journals to augment human review and generate reports flagging duplications and abnormalities for editors to inspect ^{[6] [1]}, and other publishers report using ImageTwin / ImaChek / Proofig to screen submissions before publication ^[2].

2. Specialized tools researchers and institutions deploy

Beyond vendor products, academic groups and consortia are building computational pipelines that extract images from PDFs and apply deep‑learning and forensic algorithms to detect copied, pasted, or otherwise manipulated regions; examples include a UNICAMP‑led consortium and open research projects aimed at flagging manipulations and paper‑mill signatures—these tools can sometimes outperform naked‑eye inspection in sensitivity but also generate false positives that require human follow‑up ^{[4] [7]}.

3. Practical, often free, institutional checks to request or run

Editorial and oversight units rely on a mix of software and simple file‑level checks: the NIH/ORI toolset and Division of Investigative Oversight workflows (including examining embedded/underlying images via Office “Reset Picture” and other forensic tricks) are part of many institutions’ arsenals, and are cited as practical, low‑cost methods that have uncovered reused or hidden images in PowerPoint and manuscript files ^{[5] [8]}.

4. Strengths, limitations and the vendor incentive to sell certainty

Automated detectors scale and catch many duplications that human reviewers miss, but they are imperfect—accuracy varies by image type and dataset, some tools produce false positives and struggle with novel manipulations, and experts warn that increasingly sophisticated edits will outpace current detectors unless training data and methods keep evolving ^{[7] [8] [4]}. The commercial players (Proofig, ImageTwin/Imagetwin, ImaChek) fill an urgent demand from journals, which creates a commercial incentive to position these tools as essential—an implicit agenda worth noting when reviewing vendor claims ^{[2] [3]}.

5. Fabricated citations: a gap in the available browser‑extension landscape

The supplied reporting focuses on image forensics and paper‑mill detection; there is little documented evidence here of browser extensions that reliably detect fabricated, invented, or misattributed citations in health articles, so the practical response recommended in the literature is process‑driven rather than extension‑driven: use automated paper‑mill detectors (e.g., Papermill Alarm) and publisher editorial checks to spot suspicious textual or citation patterns, cross‑check references manually against source journals, and require raw data from authors where possible ^{[4] [5]}. Where the sources are silent, it cannot be asserted that a robust browser extension already exists for fabricated‑citation detection—reporting points instead to institutional screening workflows and specialized services.

6. Recommended workflow for skeptical readers and editors

Combine an automated image pass (Proofig, ImageTwin/Imagetwin or institutional AI tools) to flag duplication and splicing, run PDF‑extraction forensic checks developed by research groups for harder cases, use NIH/ORI forensic tactics for embedded images, and escalate flagged items for human review with a request for raw images—while for suspected citation fabrication rely on editorial checks, paper‑mill detectors and direct verification of cited sources rather than expecting a single browser add‑on to do the job ^{[1] [3] [5] [4]}. The tools speed detection but do not remove the need for skeptical, expert adjudication.