A Comprehensive Review on Deep Fake Detection in Videos

The last few decades have seen a significant rise in Artificial Intelligence (AI) and Machine Learning (ML), promoting the development of deepfake technology. Deepfakes are synthetic media created using AI techniques, altering audio, images, and videos to appear authentic but are fabricated. Employing concepts like Generative Adversarial Networks (GANs), deepfake creation involves a competitive process where one model produces forgeries while another aims to identify them. The consequences of deepfakes are extensive, ranging from misinformation campaigns by terrorist organizations to individual harm through fabricated pornography and financial scams. Detecting deepfakes presents a significant challenge, prompting exploration into various methodologies. Convolutional Neural Networks (CNNs) stand as a prominent tool, with studies comparing different architectures such as DenseNet, VGGNet, ResNet, and custom CNNs. Additionally, autoencoders, particularly Variational Autoencoders, offer utility in reducing data dimensionality and editing facial features in images. Transfer learning, where pre-trained models like InceptionV3 are adapted for deepfake detection, and multi-model approaches combining CNNs with Long-Short Term Memory Networks (LSTMs) have shown promise. Furthermore, novel techniques like CapsuleNets and ensemble methods, which combine multiple base models, are explored for their efficacy. CapsuleNets facilitate spatial inconsistency detection, while ensembling aims to enhance model robustness. Two-stream networks, integrating spatial and frequency streams, offer a solution for handling low-quality videos. Despite there being various advancements in detection models, deepfake detection faces numerous challenges. Adverse serial attacks targeting detection models, the emergence of new deepfake variants evading existing detection mechanisms, and computational resource requirements hindering real-time implementation are significant hurdles. Moreover, concerns regarding privacy, fairness, and biases in training data persist. As deepfake generation technology progresses, detection models may struggle to keep pace, requiring ongoing research and adaptation. While detection models play a crucial role, a comprehensive approach to addressing deepfake-related issues is imperative. This involves not only refining detection techniques but also fostering public awareness, implementing regulatory measures, and promoting responsible usage of AI-generated content. By addressing these versatile challenges, society can reduce the negative impacts of deepfake technology.