Univrsity Of Tehran Press Journal of Information Technology Management 2980-7972 18 1 2026 03 01 Adaptive Machine Learning Framework for Deepfake Detection: An Ensemble-Driven Approach with Optimized Feature Engineering 53 79 106254 10.22059/jitm.2026.106254 EN Shruthi S Research Scholar, Computer Science Engineering, R R Institute of Technology, Visvesvaraya Technological University, Belagavi, India. Manjunath R Professor and Head, Computer Science Engineering, R R Institute of Technology, Bangalore, Visvesvaraya Technological University, Belagavi, India. Journal Article 2026 03 06 With the rapid progress of artificial intelligence, it's now possible to generate extremely undoubted DeepFake images and videos, leading to major concerns related to misinformation, identity theft, and online security. Detecting such manipulated media requires robust and efficient techniques. Deep learning models, particularly Convolutional Neural Networks (CNNs), have been confirmed to be highly effective, but they typically demand high computational resources and may suffer from overfitting issues. "This study presents a Machine Learning based approach for the detection of DeepFakes, utilizing Histogram of Oriented Gradients (HOG), utilizing Principal Component Analysis (PCA) to decrease the dimensionality, and employing a combination of Random Forest and XGBoost classifiers for the final prediction. The DeepFake Detection Challenge Dataset, sourced from Kaggle, is employed for both training and performance assessment. The methodology consists of feature extraction using HOG to capture texture and gradient information, dimensionality reduction with PCA to enhance computational efficiency, and ensemble classification with Random Forest and XGBoost to improve detection accuracy. Hyperparameter tuning is performed to further optimize model performance. The investigational results prove that the anticipated hybrid ML model accomplishes an accuracy of 95.5%, outperforming conventional standalone classifiers and providing a computationally efficient alternative to deep learning-based approaches. This study highlights the efficiency of merging traditional feature-engineering techniques with ensemble learning for DeepFake detection. The results contribute to ongoing research in AI-driven security and media forensics, offering a scalable, interpretable, and high-performance solution for identifying manipulated media. Future work can explore additional feature extraction techniques and ensemble models to further enhance detection capabilities. https://jitm.ut.ac.ir/article_106254_b898ca3ec9b7eb14f35c304df1bccabd.pdf