Deepfake is a critical problem that poses a significant threat to the authenticity and integrity of visual media. Although computer vision and pattern recognition have been employed in previous studies, there exist limitations on the quantity of layers that are permissible for use. This article proposes a strategy based on decentralized blockchain to create a secure and reliable system that can determine the authenticity of content in order to tackle this problem. tracing the original video, and safeguarding media integrity from identity theft. The proposed approach employs a combination of technologies including Blockchain, the encryption, media filtering, The proposed approach utilizes the The Convolutional Neural Network, Consensus Algorithm, and SHA-256 Hashing Algorithm are employed to ensure the protection and integrity of the media. Additionally, the implementation of a Deepfake Analyzer is recommended to augment media protection. The proposed method's efficacy is assessed by utilizing an available dataset from GitHub, and the outcomes are extensively examined. This research offers valuable perspectives on how blockchain technology can be leveraged to safeguard the integrity of images, videos, and reduce the spread of misinformation in the media. The proposed Deepfake Analyzer adds an additional layer of protection, ensuring video and image integrity against identity theft.