Faysal-MD/Unmasking-Deepfake-Faces-from-Videos-An-Explainable-Cost-Sensitive-Deep-Learning-Approach-IEEE2023
Deepfake faces detection from forged videos where used explainable AI for models' robustness as well as cost sensitive methods for mitigating dataset imbalance problem
This project helps verify the authenticity of video content by detecting manipulated deepfake faces. It takes a video as input and identifies whether the faces within it are real or synthetically generated. This is useful for fact-checkers, journalists, or content moderators who need to assess the trustworthiness of video evidence.
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Use this if you need to determine with high confidence whether faces in a video have been digitally altered using deepfake technology.
Not ideal if you are trying to detect other forms of video manipulation beyond deepfake faces, such as audio alteration or scene changes.
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May 27, 2024
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