Targeted Data Extraction and Deepfake Detection with Blockchain Technology

Abstract

By recording instances of significant forensic relevance, smartphones, which are becoming increasingly crucial for documenting ordinary life events, can produce pieces of evidence in court. Due to privacy or other issues, not everyone is open to having all the data on their phone collected and analyzed. In addition, Law Enforcement Organizations need a lot of memory to keep the information taken from a witness’s phone. Deepfakes which are purposefully utilized as a source of disinformation, manipulation, harassment, and persuasion in court, present another significant problem for law enforcement organizations. Recently, the introduction of blockchain has altered the way we conduct business. Decentralized Applications (Dapps) may be a fantastic way to verify the accuracy of the data, stop the spread of false information, extract specific data with precision, and offer a framework for sharing that takes into account privacy and memory issues. This article outlines the creation of a Dapp that provides users with a secure conduit through distributing evidence that has been verified. By utilizing machine learning (ML) classifiers, this platform not only distinguishes between altered and original material before allowing it, but also uses user-uploaded media to retrain its models to increase prediction accuracy and offer complete transparency. The end outcome of this activity can maintain a clear record (timestamp) of the occurrence, submitted proof, and helpful metadata with the aid of the blockchains’ consensus notion.