The accelerated development of deepfake technology has generated tremendous concern regarding its potential abuse in cyber influence operations. Deepfake-created media, such as doctored videos and fake audio, are increasingly utilized to manipulate audiences, influence public opinion, and propagate disinformation. This study discusses the use of artificial intelligence (AI) to detect and prevent deepfake deception. We offer an extensive review of AI-based deep fake detection techniques, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformer models (figure-1). In addition, we introduce mitigation methods, such as blockchain-based authentication, adversarial training, and forensic watermarking. The results underpin the critical role of AI in combating the emergence of deepfake deception and calling for enhanced detection frameworks to protect digital information integrity.
Deepfake detection, Artificial intelligence, Cyber influence operations, Synthetic media, Adversarial networks, Digital forensics, Machine learning, Forgery detection, GANbased attacks.