The Impact of Deepfakes on Digital Media Authenticity

Abstract: With digital manipulation blurring the lines between reality and fabrication, deepfakes have become one of the most shocking threats to media authenticity and public trust. Fed by advanced artificial intelligence techniques like Generative Adversarial Networks (GANs) and deep learning, deepfakes can create hyper-realistic videos, images, and audio that convincingly imitate real people. These forgeries have far-reaching implications, from spreading political misinformation and financial fraud to non-consensual explicit content and identity theft. As deepfake technology becomes more sophisticated and accessible, traditional detection methods struggle to keep pace, sparking an urgent technological arms race between creators and defenders. This survey discusses the rapidly changing landscape of deepfake detection, focusing on multimodal approaches that combine visual, audio, and physiological cues to improve detection accuracy. Combining facial landmark analysis, voice consistency checks, and even heart rate variability extraction, multimodal detection methods provide a strong defense against increasingly complex forgeries. We discuss state-of-the-art models, including Convolutional Neural Networks (CNNs), Vision Transformers, and XGBoost classifiers, along with their evaluation against challenging datasets like Celeb-DF and FaceForensics++. Lastly, the paper emphasizes the problems in current detection techniques, especially in real- time processing and the generalization problems of deepfakes across a wide variety of generation methods. This paper helps researchers and practitioners navigate the battlefield of digital deception by identifying the critical gaps of existing research and proposing future directions.