Deepfake Detection in Video and Audio Clips: A Comprehensive Survey and Analysis

Deepfake (DF) technology has emerged as a major concern due to its potential for misuse, including privacy violations, misinformation, and threats to the integrity of digital media. While significant progress has been made in developing deep learning (DL) algorithms to detect DFs, effectively distinguishing between real and manipulated content remains a challenge due to the rapid evolution of DF generation techniques. This study aims to address two key issues: the need for a comprehensive review of current DF detection methods and the challenge of achieving high detection accuracy with low computational cost. We conducted a systematic literature review to evaluate various DF detection algorithms, focusing on their performance, computational efficiency, and robustness. The review covers methods such as Convolutional Neural Networks (CNNs), Long Short Term Memory (LSTM) networks, hybrid models, and specialized approaches like spectral and phonetic analysis. Our findings reveal that while some methods achieve high accuracy, up to 94% in controlled environments, they often struggle to generalize across diverse DF applications. Hybrid models that combine CNNs and LSTMs typically offer a better balance between accuracy and computational efficiency. This paper provides valuable insights into the current state of DF detection and highlights the need for adaptive models that can effectively address the evolving challenges of DF generation.