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@article{165140,
        author = {Aastha Bhandari and Monika Chauhan  and Dr. Sachil Kumar  and Avni Goel },
        title = {Forensic Analysis Techniques for Deepfake Investigation},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {11},
        number = {1},
        pages = {547-552},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=165140},
        abstract = {In an era marked by the rampant proliferation of deepfake technology, our world is grappling with unprecedented challenges in safeguarding the authenticity of digital content. This review paper endeavors to shed light on the intricate landscape of deepfake creation and dissemination, offering a comprehensive overview of forensic analysis techniques tailored specifically for investigating manipulated media. The paper will explore detection, verification, and attribution methodologies, exploring the underlying principles of both traditional and cutting-edge approaches. From machine learning and facial biometrics to image and video manipulation detection, audio analysis, and data provenance tracking, we dissect the arsenal of tools available for combatting the rising tide of deepfake threats.
To reduce bias a comprehensive literature search and objective assessment of the retrieved papers is performed. A search was conducted in the Google Scholar, PubMed, MEDLINE, Scopus, and Science Direct databases until 2022. The search keywords include various combinations like digital forensics, forensic science, deepfake technology, algorithms and artificial intelligence This systematic review critically reviewed 60 relevant studies.
},
        keywords = {Autoencoders, VAEs, Image Analysis, RNNs, Digital Evidence },
        month = {},
        }