The rise of Deepfake technology in the information age provided significant challenges in ensuring the reliability and authenticity of digital media content. Deepfake pictures, videos, and audio recordings have reached advanced levels of sophistication, making it challenging to distinguish authenticity from fraudulent activity. Deepfakes are highly manipulated images, audio recordings, and videos that use artificial intelligence to create convincing forgeries of individuals engaging in actions or making statements they never actually performed. This advanced technology has multiple applications, including entertainment and social media, as well as potentially harmful activities like propagandism and disinformation. Innovative AI techniques, such as deep learning, transfer learning, Long Short-Term Memory (LSTM) networks, and Convolutional Neural Networks (CNNs), have been developed to effectively detect and combat Deepfakes. Deep learning algorithms, specifically CNNs, which are inspired by the visual cortex of the human brain, have proven to be effective in detecting Deepfake images and videos. The solution to this problem is to use collective techniques that improve detection accuracy by combining multiple AI systems. Addressing this challenge involves employing a combination of techniques to enhance detection accuracy, such as leveraging the strengths of CNNs and LSTM models in addition to other techniques like Generative Adversarial Networks (GANs). The primary goal is to provide studies on protecting the integrity and authenticity of digital content using different algorithms and standard datasets, while also investigating potential future developments in the field. AI techniques effectively detect Deepfake in various media, improving digital content authenticity. Deepfake technology’s growth presents risks in fraud and misinformation, urging advancements in detection methods. The study advocates for collaborative efforts and legal reforms, especially in India, to combat Deepfake challenges.

The rapid growth of digital communication and video transmission has brought about an urgent need for robust encryption methods to ensure the security and integrity of video data. Traditional encryption techniques, while effective, face increasing challenges from advancing computing capabilities. Quantum cryptography has emerged as a promising solution to secure video transmission. In this article, we propose a novel cutting-edge Hybrid Quantum Video Encryption Framework. Our key idea is to integrate the strengths of quantum encryption and the classical video transmission paradigm to enhance the security of video transmission. By combining the proposed quantum encryption framework which uses a pseudorandom number generated key to perform a row-wise XOR operation along with the secure SSL-encrypted HTTP transmission over the internet, this framework provides a robust defense against threats. The experimental simulation conducted under the new computational integrated paradigm shows the strong encryption capability of the proposed method. The superior performance of the proposed method (ranges from 10-15% compared to the existing methods) as validated through statistical analysis, outperforms the state-of-the-art video encryption frameworks in terms of Information Entropy and Correlation Coefficient for encrypted plain images offering a significant advancement in the field of secure video transmission.