kashiani/Face-Morphing-Attack-Detection-Benchmark
Face Morphing Attack Detection Benchmark (IJCB 2022: Robust Ensemble Morph Detection with Domain Generalization)
This project helps security and border control professionals identify 'morphed' facial images, which are fraudulent images created by combining multiple faces. It takes in digital facial images and outputs a detection result indicating whether the image is legitimate or a morphing attack. Border control agents, passport issuing authorities, and forensic investigators would find this useful for verifying identities.
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Use this if you need to test and evaluate deep learning models designed to detect fraudulent facial morphing attacks for identity verification.
Not ideal if you are looking for a ready-to-deploy, out-of-the-box solution for real-time morphing detection in a production environment without any development effort.
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Dec 18, 2024
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