A Systematic Survey of Deepfake Harassment Using Digital Forensics

The rapid advancement of synthetic media technologies has made deepfake harassment a serious cybersecurity and societal concern. Deepfake-based abuse, including non-consensual explicit content and identity impersonation, causes severe psychological, social, and reputational harm to victims. Most existing solutions rely on artificial intelligence and machine learning techniques for detecting manipulated media; however, these approaches are often computationally expensive, dataset-dependent, and unsuitable for forensic or legal use. This project proposes a cybersecurity- centric framework that treats deepfake harassment as a digital forensics problem rather than a prediction task. The system focuses on media integrity verification, metadata forensic analysis, cryptographic hashing, provenance tracking, and secure chain-of-custody mechanisms. A structured reporting workflow enables the generation of forensic integrity reports that support investigation and evidence handling. The proposed framework is implemented as a working prototype and evaluated using controlled test cases to demonstrate its ability to flag suspicious media and preserve reliable digital evidence, thereby strengthening trust in digital media ecosystems.