DeDigi: A Privacy-by-Design Platform for Image Forensics
Chi-Hao Tran, Quoc-Thang Tran, Quynh-Chau Long-Vu, Hai Son Nguyen, Anh-Duy Tran, Duc‐Tien Dang‐Nguyen · 2022
With the explosion of multimedia has come the rise of advanced image processing and free editing tools, allowing individuals to readily change how an image or video appears in front of other eyes. This act is malicious and may cause disruption in the community; therefore, to resolve these issues, there has been many existing tools that use Digital Image Forensics (DIF) techniques. Even so, based on our MUUP evaluation system, these tools can lack guidance, difficult to utilize and can have privacy issues. Following that, we then investigate existing algorithms from various DIF techniques, and incorporate this knowledge into our DIF web tool - DeDigi. For DeDigi's design, based on our Design Science system, each design iteration will take into account user feedback on how the tool's design is perceived from their perspective. The final outcome of our "privacy-by-design" tool is five approaches from various categories of the focused field, a score of 3.92/5 for DeDigi (beta)'s user experience, and new user interfaces for training purpose.