Enhancing tumor deepfake detection in MRI scans using adversarial feature fusion ensembles.

ABSTRACT: The proliferation of AI-generated medical deepfakes, such as tumor insertions or removals in diagnostic scans, threatens patient safety and healthcare integrity. Existing detection methods often lack robustness against adversarial attacks or fail to integrate multimodal feature representations. To address these gaps, we propose AFFETDS (Adversarial Feature Fusion Enhanced Tumor Detection System), a novel ensemble framework combining adversarial training, feature fusion, and weighted voting. AFFETDS leverages adversarial attack methods (PGD, FGSM) to harden the model, fuses high-level ResNet50 features with handcrafted HOG descriptors, and employs an SVM-based ensemble classifier. Evaluated on a curated dataset of 1378 MRI scans (774 real, 604 manipulated) from TCIA and ADNI repositories, A

SUBMITTER: Ali A 

PROVIDER: S-EPMC12800179 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature