Available Theses

  • DeepFake Anonymization - A Novel Approach to Privacy Preservation in Video ContentDetails


    The advent of deep learning technologies has significantly impacted fields such as computer vision and digital content creation. An innovative application of these technologies lies in the realm of privacy preservation in video content. This master's thesis explores the use of deep fake technology as a tool for anonymization in videos. The study focuses on developing a deep learning-based framework capable of replacing or modifying identifiable features in video content, like faces or voices, with realistic synthetic alternatives. The primary objective is to ensure the privacy and anonymity of individuals in video data while preserving the integrity and continuity of the visual content. The research encompasses technical aspects of deep fake creation and potential applications in various domains, including media, surveillance, and online content creation. Importantly, the thesis includes a comprehensive user study to evaluate the effectiveness and perception of these anonymization techniques. The study will also address the challenges and limitations of using deep fakes for anonymization and propose solutions to mitigate potential risks associated with this technology.

    Research Objectives:

    1. Development of a deep learning framework capable of generating high-quality deep fakes for anonymization in videos.

    2. Evaluation of the effectiveness of deep fake anonymization in preserving privacy without compromising the authenticity of the video content.


    - Literature Review: Conduct a comprehensive review of existing literature on deep fakes, privacy preservation techniques in videos, and relevant deep learning models.

    - Framework Development: Development of a deep learning framework for creating deep fakes specifically tailored for anonymization purposes. This would involve training models on diverse datasets to ensure generalizability.

    - Evaluation: Test the framework on various video datasets to assess the quality of anonymization, the preservation of content integrity, and the robustness of the anonymization against re-identification attacks. An additional critical step in the methodology will be conducting a user study. This study will gather qualitative and quantitative data on user perceptions of the anonymized videos. The study aims to understand how viewers perceive the anonymized content in terms of realism, the impact on the video's message or content, and their overall acceptance of using deep fakes for privacy preservation. This feedback will be crucial in refining the framework and understanding its potential impact on viewers.

    Expected Outcomes:

    - A fully functional deep learning framework for video anonymization using deep fakes.

    - A comprehensive evaluation of the framework's performance and perception in a user study and its limitations.

    This thesis aims to contribute to the field by providing a novel approach to privacy preservation in videos and sparking discussions on the responsible use of deep fake technology in safeguarding personal privacy.

    Master Thesis, Computer Science, Tutor: Marvin Strauß, M.Sc.