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The widespread use of deepfake technology has created serious obstacles for multimedia content integrity, necessitating the urgent necessity for reliable detection methods. This study examines how well-advanced techniques like watermark embedding, metadata verification, and hashing work when combined with a Deep Fake Lab to improve the detection of manipulated media. The solution under consideration makes use of cryptographic hashing techniques to generate unique IDs for authentic multimedia content. Advantages of referring to hashes; The algorithm may also detect hashes indicating possible deepfake manipulations by computing hashes of questionable media and comparing them to reference hashes of high-quality sources. Moreover, metadata is studied to detect abnormalities in the metadata of media files that are inherent to the image such as timestamps, camera settings, and timestamps which are comparable to deepfake manipulation. Moreover, during the development process, the various ways you can integrate watermarks (which are small, indistinguishable electronic footprints) are also explored. Despite its potential for distortion of its image, these watermarks work as reliable determinants of accuracy and can be helpful for establishing whether any changes or manipulations have been made illegally. The above techniques provide a broad range of measures for estimating and eliminating the spread of deepfakes on different media platforms through the use of a centralized Deep Fake Lab. The effectiveness of the proposed framework with regard to identifying various forms of altered or harmful media is then examined in light of detection accuracy, resistance to adversarial attacks, and computational complexity through extensive testing utilizing diverse datasets of legitimate or altered media. Some of the key findings of the research provide an opportunity for greater support in ensuring that audiovisual information remains intact in a world that is increasingly in a digital age as well as giving rise to improved technologies for the development of deepfake detection.