We lifted 5 functions out of this paper's own repositories and ran 4 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| zblasingame/Greedy-DiM | canonical | 4 of 5 |
| Function | Status | Where it lives |
|---|---|---|
| betas_for_alpha_bar | Ran | zblasingame/Greedy-DiM/scheduler.py code served (permissive licence) · get_code("10c16e505869b0f3") |
| conv1x1 | Ran | zblasingame/Greedy-DiM/arcface/iresnet.py code served (permissive licence) · get_code("158bf4c3a5f11f04") |
| conv3x3 | Ran | zblasingame/Greedy-DiM/arcface/iresnet.py code served (permissive licence) · get_code("29df79c9fdb0cee8") |
| rescale_zero_terminal_snr | Ran | zblasingame/Greedy-DiM/scheduler.py code served (permissive licence) · get_code("825af7863abcbfe2") |
| iresnet18 | Not yet run | zblasingame/Greedy-DiM/arcface/iresnet.py code served (permissive licence) · get_code("000c54655537fe64") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
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 Varational 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.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2404.06025")
get_code_for_paper("2404.06025")
have("2404.06025")
Connect an agent — have() is free.