Rescind: Countering Image Misconduct in Biomedical Publications with Vision-Language and State-Space Modeling

TL;DR

This paper introduces Rescind, a novel framework combining vision-language models and state-space techniques to generate and detect biomedical image forgeries, enhancing research integrity.

Contribution

It presents the first vision-language guided approach for biomedical image forgery detection and introduces Rescind, a comprehensive benchmark with a new structured localization framework.

Findings

Integscan achieves state-of-the-art detection accuracy.

Rescind provides detailed annotations for biomedical images.

The method effectively localizes diverse forgery types.

Abstract

Scientific image manipulation in biomedical publications poses a growing threat to research integrity and reproducibility. Unlike natural image forensics, biomedical forgery detection is uniquely challenging due to domain-specific artifacts, complex textures, and unstructured figure layouts. We present the first vision-language guided framework for both generating and detecting biomedical image forgeries. By combining diffusion-based synthesis with vision-language prompting, our method enables realistic and semantically controlled manipulations, including duplication, splicing, and region removal, across diverse biomedical modalities. We introduce Rescind, a large-scale benchmark featuring fine-grained annotations and modality-specific splits, and propose Integscan, a structured state space modeling framework that integrates attention-enhanced visual encoding with prompt-conditioned semantic alignment for precise forgery localization. To ensure semantic fidelity, we incorporate a vision-language model based verification loop that filters generated forgeries based on consistency with intended prompts. Extensive experiments on Rescind and existing benchmarks demonstrate that Integscan achieves state of the art performance in both detection and localization, establishing a strong foundation for automated scientific integrity analysis.