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-<!--# Greedy-DiM - Official PyTorch Implementation-->
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-<!--![Static Badge](https://img.shields.io/badge/last%20update-2024.09.26-blue)-->
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-<!--![Teaser image](./docs/assets/greedy_dim_morph_comp.png)-->
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-<!--This repository contains the offical PyTorch implementation of the following paper:-->
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-<!--> **Greedy-DiM: Greedy Algorithms for Unreasonably Effective Face Morphs**<br>-->
-<!--> Zander W. Blasingame and Chen Liu<br>-->
-<!--> [https://arxiv.org/abs/2404.06025](https://arxiv.org/abs/2404.06025)-->
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-<!--> Abstract: *Morphing attacks are an emerging threat to state-of-the-art Face Recognition (FR) systems, which aim to create a single image that contains the biometric information of multiple identities. Diffusion Morphs (DiM) are a recently proposed morphing attack that has achieved state-of-the-art performance for representation-based morphing attacks. However, none of the existing research on DiMs have leveraged the iterative nature of DiMs and left the DiM model as a black box, treating it no differently than one would a Generative Adversarial Network (GAN) or Variational AutoEncoder (VAE). We propose a greedy strategy on the iterative sampling process of DiM models which searches for an optimal step guided by an identity-based heuristic function. We compare our proposed algorithm against ten other state-of-the-art morphing algorithms using the open-source SYN-MAD 2022 competition dataset. We find that our proposed algorithm is unreasonably effective, fooling all of the tested FR systems with an MMPMR of 100%, outperforming all other morphing algorithms compared.*-->
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- <!--Along with containing the code for our previous papers [DiM](https://ieeexplore.ieee.org/document/10381591) and [Fast-DiM](https://ieeexplore.ieee.org/document/10569993).-->
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 <h1> Greedy-DiM (IJCB 2024) </h1>
 <h3> Greedy-DiM: Greedy Algorithms for Unreasonably Effective Face Morphs </h3>