MedForge: Interpretable Medical Deepfake Detection via Forgery-aware Reasoning
arXiv:2603.18577v1 Announce Type: new Abstract: Text-guided image editors can now manipulate authentic medical scans with high fidelity, enabling lesion implantation/removal that threatens clinical trust and safety. Existing defenses are inadequate for healthcare. Medical detectors are largely black-box, while MLLM-based explainers are typically post-hoc, lack medical expertise, and may hallucinate evidence on ambiguous cases. We present MedForge, a data-and-method solution for pre-hoc, evidence-grounded medical forgery detection. We introduce MedForge-90K, a large-scale benchmark of realistic lesion edits across 19 pathologies with expert-guided reasoning supervision via doctor inspection guidelines and gold edit locations. Building on it, MedForge-Reasoner performs localize-then-analyze reasoning, predicting suspicious regions before producing a verdict, and is further aligned with Forgery-aware GSPO to strengthen grounding and reduce hallucinations. Experiments demonstrate state-of
arXiv:2603.18577v1 Announce Type: new Abstract: Text-guided image editors can now manipulate authentic medical scans with high fidelity, enabling lesion implantation/removal that threatens clinical trust and safety. Existing defenses are inadequate for healthcare. Medical detectors are largely black-box, while MLLM-based explainers are typically post-hoc, lack medical expertise, and may hallucinate evidence on ambiguous cases. We present MedForge, a data-and-method solution for pre-hoc, evidence-grounded medical forgery detection. We introduce MedForge-90K, a large-scale benchmark of realistic lesion edits across 19 pathologies with expert-guided reasoning supervision via doctor inspection guidelines and gold edit locations. Building on it, MedForge-Reasoner performs localize-then-analyze reasoning, predicting suspicious regions before producing a verdict, and is further aligned with Forgery-aware GSPO to strengthen grounding and reduce hallucinations. Experiments demonstrate state-of-the-art detection accuracy and trustworthy, expert-aligned explanations.
Executive Summary
The article 'MedForge: Interpretable Medical Deepfake Detection via Forgery-aware Reasoning' presents a novel approach to medical forgery detection, addressing the critical need for trustworthy and explainable methods in healthcare. MedForge utilizes a large-scale benchmark and forgery-aware Generalized Score-based Posterior Operator (GSPO) to achieve state-of-the-art detection accuracy and aligned explanations. The proposed method, MedForge-Reasoner, performs localized analysis before producing a verdict, reducing hallucinations and improving trustworthiness. The article highlights the inadequacy of existing defenses in healthcare and the importance of interpretable AI in medical fields. The authors' solution provides a promising direction for ensuring the integrity and reliability of medical scans, underscoring the significance of explainable AI in high-stakes applications.
Key Points
- ▸ MedForge presents a novel approach to medical forgery detection
- ▸ Utilizes a large-scale benchmark, MedForge-90K, for realistic lesion edits
- ▸ Introduces forgery-aware Generalized Score-based Posterior Operator (GSPO) for strengthened grounding and reduced hallucinations
Merits
Strength in Methodological Approach
The article's emphasis on interpretable AI and explainable methods is a significant contribution to the field, addressing the critical need for trustworthy medical forgery detection.
Advancements in Detection Accuracy
MedForge-Reasoner demonstrates state-of-the-art detection accuracy, highlighting the method's potential for real-world applications in healthcare.
Expert-Aligned Explanations
The forgery-aware GSPO and localized analysis in MedForge-Reasoner provide trustworthy and expert-aligned explanations, essential for clinical trust and safety in medical fields.
Demerits
Limited Generalizability
The article's focus on a specific dataset and benchmark may limit the generalizability of MedForge to other medical imaging applications or real-world scenarios.
Dependence on High-Quality Training Data
The performance of MedForge-Reasoner relies on the availability and quality of high-quality training data, which might be challenging to obtain in certain medical imaging contexts.
Expert Commentary
The article presents a notable advancement in medical forgery detection, leveraging a novel approach that emphasizes interpretable AI and explainable methods. The proposed method, MedForge-Reasoner, demonstrates impressive detection accuracy and trustworthy explanations, addressing a critical need in healthcare. While the article's focus on a specific dataset may limit generalizability, the method's potential for real-world applications and the emphasis on expert-aligned explanations make it a significant contribution to the field. Future work should focus on addressing the dependence on high-quality training data and exploring the scalability of MedForge-Reasoner in various medical imaging contexts.
Recommendations
- ✓ Further research should explore the application of MedForge-Reasoner in diverse medical imaging contexts, including real-world scenarios and various disease types.
- ✓ Development of standardized evaluation protocols and benchmarks for medical forgery detection, ensuring the reproducibility and comparability of results across different methods.
