Identification of Fake Faces Using Deep Learning

The rise of deepfake technology has introduced significant challenges to the authenticity of digital media, enabling the creation of highly convincing yet deceptive audio, video, and image content. This survey examines recent progress in detecting and mitigating deepfakes across visual, auditory, and multimodal biometric frameworks. Techniques such as convolutional neural networks for image processing, temporal analysis for video sequences, and spectral feature extraction for audio verification have been widely explored. Emerging methods, including Face X-ray techniques, combined CNN-LSTM models, and integrated multimodal approaches, show promise in identifying synthetic media. Nevertheless, issues such as dataset generalization, real-time detection capabilities, and vulnerability to adversarial manipulations remain critical hurdles. This paper provides an in-depth analysis of current methodologies, their comparative effectiveness, limitations, and future directions for enhancing digital content verification systems