A Hybrid CNN–LSTM Framework with Explainable AI for Robust Deepfake Detection
TL;DR
A robust, explainable detection framework is presented that combines a CNN backbone for extracting spatial artifacts with an LSTM module for modeling temporal inconsistencies across frames that enhances forensic decision support and increases practical readiness for content verification systems.
Abstract
Deepfakes pose growing risks to information integrity, yet many detectors perform well only on the datasets they were trained on and remain opaque to human analysts. A robust, explainable detection framework is presented that combines a CNN backbone for extracting spatial artifacts with an LSTM module for modeling temporal inconsistencies across frames. To make decisions auditable, the architecture incorporates Grad-CAM for spatial heatmaps, SHAP for quantitative feature attribution, and LIME for local surrogate explanations. The system was trained primarily on FaceForensics++ with stratified sampling and augmentation to reduce dataset bias and evaluated on multiple external benchmarks to assess cross-domain generalization. Experimental results show strong detection metrics, such as accuracy of 96.3%, precision of 95.8%, recall of 96.7%, and an F1-score of 96.2%, along with robust performance under JPEG compression, Gaussian noise, and FGSM adversarial attacks. By coupling high detection accuracy with transparent explanations, the proposed approach enhances forensic decision support and increases practical readiness for content verification systems.