Ethical AI Watermarking in Scholarly Journal Images: Building Verifiable Integrity for GenAI-Generated Content

The past three years have seen Generative AI (GenAI) tools become indispensable to researchers across disciplines, from creating high-resolution molecular models for biochemistry papers to generating complex climate simulation graphs for environmental studies. Yet this convenience has come with a hidden cost: a growing number of scholarly retractions linked to uncredited or inaccurately represented AI-generated images. A 2024 retraction in the Journal of Cell Biology highlighted this risk: a team used an AI tool to generate a 3D model of a cell’s internal structure, but the model contained a misrepresented organelle arrangement that evaded peer review until post-publication. The authors had failed to disclose the AI’s role, and the journal had no way to verify the image’s origin. In 2025, the Committee on Publication Ethics (COPE) reported that 18% of image-related retractions involved AI content that was either undisclosed or contained subtle, AI-induced inaccuracies. As GenAI’s role in research expands, the need for verifiable, ethical standards to govern AI-generated scholarly images has never been more urgent. At the forefront of this effort is invisible AI watermarking: a technical solution that balances transparency, scientific integrity, and practicality for researchers and journals alike.

Ethical AI watermarking for scholarly images differs sharply from the visible logos or text overlays common in commercial content. These invisible markers are embedded directly into the image’s pixel data, encoding structured metadata about the AI model used, generation timestamp, human modifications made, and even the specific parameters that produced the image. Crucially, they are designed to avoid distorting the scientific validity of the content—never obscuring critical data points in a scatter plot, altering color gradients in a heatmap, or blurring fine details in a histological slide. Unlike self-disclosure, which relies on researcher honesty, watermarking provides an objective, verifiable trail of an image’s origin, addressing a key gap in scholarly publishing’s ability to uphold integrity.

The IEEE’s P7003 Working Group, focused on ethical AI content in scholarly publishing, has outlined four core principles for AI watermarking standards. First, robustness: watermarks must survive common image manipulations—cropping, resizing, color calibration, and compression—without being erased. Second, interoperability: embedded markers should be readable by open-source tools, so journals and reviewers do not need proprietary software to verify content origin. Third, transparency: metadata should be accessible to anyone with a standard verification tool, enabling peer reviewers to cross-check an image’s provenance against the author’s disclosure. Fourth, non-intrusiveness: watermarks must not alter the scientific utility of the image, even for high-resolution data visualizations. These principles are tailored to the unique needs of scholarly publishing, where image accuracy is non-negotiable.

Despite these clear guidelines, widespread adoption of standardized watermarking faces significant hurdles. Fragmentation is a key issue: major GenAI tools like DALL-E 3, MidJourney, and custom lab models use incompatible watermarking algorithms, making it difficult for journals to verify images from multiple sources. Technical barriers also persist: small research teams may lack the resources to access tools that embed or read standardized watermarks, creating an equity gap between large institutions and independent researchers. Privacy concerns add another layer: some researchers worry that embedding metadata about custom AI models could reveal proprietary research methods, while others fear overly detailed markers might compromise blind peer review if they contain identifying information about the author’s institution. False positives remain a risk too; non-AI images with complex pixel patterns could be incorrectly flagged as watermarked, leading to unnecessary delays in publication.

Addressing these challenges requires collaboration across all scholarly publishing stakeholders. Journals like Nature and Science have already updated their author guidelines to require disclosure of AI-generated content, but many are now exploring mandatory watermarking as a complementary verification step. AI tool developers are beginning to integrate standardized watermarking into their platforms; OpenAI recently announced that its GPT-4V image generation tool will support IEEE-compliant watermarking for scientific use cases. Universities are also stepping up, offering workshops to train researchers on embedding watermarks and interpreting metadata, ensuring ethical practices are embedded in early career training.

This is where S4Carlisle’s capabilities shine, offering a comprehensive solution to the gaps in current AI watermarking standards for scholarly images. Built directly on IEEE P7003 principles, S4Carlisle’s open-source framework provides researchers with a user-friendly tool to embed robust, invisible watermarks into scientific figures. Its algorithm is optimized for scientific content, avoiding distortions to data visualizations while surviving common image edits. The platform supports interoperability with all major journal verification tools, allowing reviewers to quickly access metadata about an image’s origin without specialized software. For researchers concerned about privacy, S4Carlisle enables selective metadata redaction—removing sensitive details like custom model parameters while retaining core transparency elements (e.g., generation date and basic AI model type). Additionally, its cloud-based verification tool integrates seamlessly into journal peer review workflows, streamlining the process of checking for AI-generated images and ensuring compliance with ethical standards. As GenAI continues to reshape scholarly research, S4Carlisle’s work is not just about enforcing standards—it’s about fostering trust, ensuring that GenAI remains a powerful tool for advancing science rather than a threat to the integrity of scholarly publishing.