Abstract

Deepfake technology has evolved significantly since 2017, challenging the authenticity of media and digital security. This review analyzes deepfake detection methods in three categories: deep learning, machine learning, and traditional image processing. Deep learning approaches (Transformers, Attention Mechanisms, Capsule Networks, CNNs, GANs) show potential but struggle with second-generation fakes. Machine learning methods (SVM, Random Forest, KNN, Logistic Regression) offer efficient alternatives with high accuracy in controlled settings. Traditional techniques (DCT, PFA, DFT, Edge Detection, and FTWA) identify artifacts in frequency and spatial domains. Recent innovations like HashShield (99.2% accuracy), Dynamic Difference Learning, and the FAMM framework show promise for addressing temporal inconsistencies, while Head Pose Estimation effectively identifies alignment issues in manipulated expressions. Challenges include cross-dataset generalization, compression artifacts, and rapid technique evolution. Hybrid approaches and better datasets are needed to combat this growing threat.