Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
SLIP: Spoof-Aware One-Class Face Anti-Spoofing with Language Image Pretraining
Authors: Pei-Kai Huang, Jun-Xiong Chong, Cheng-Hsuan Chiang, Tzu-Hsien Chen, Tyng-Luh Liu, Chiou-Ting Hsu
AAAI 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our extensive experiments and ablation studies support that SLIP consistently outperforms previous one-class FAS methods. We conduct extensive experiments on seven public face anti-spoofing databases |
| Researcher Affiliation | Academia | 1 National Tsing Hua University, Taiwan 2 Academia Sinica, Taiwan |
| Pseudocode | No | The paper describes the methodology using textual explanations and mathematical equations, but does not include a distinct section or figure explicitly labeled "Pseudocode" or "Algorithm". |
| Open Source Code | Yes | Code https://github.com/Pei-Kai Huang/AAAI25-SLIP |
| Open Datasets | Yes | We conduct extensive experiments on the following face anti-spoofing databases: (a) OULU-NPU (Boulkenafet et al. 2017) (denoted by O), (b) CASIA-MFSD (Zhang et al. 2012) (denoted by C), (c) MSU-MFSD (Wen, Han, and Jain 2015) (denoted by M), (d) Idiap Replay-Attack (Chingovska, Anjos, and Marcel 2012) (denoted by I), (e) 3DMAD (Erdogmus and Marcel 2014) (denoted by D) , (f) HKBU-MARs (Liu et al. 2016b) (denoted by H) , (g) CASIA-SURF (Yu et al. 2020a) (denoted by U), and (h) PADISI-Face (Rostami et al. 2021) (denoted by P). |
| Dataset Splits | Yes | We conduct intra-domain testing on OULU-NPU... to design four challenging protocols for evaluating the effectiveness of the anti-spoofing models. ... In Table 3, we conduct leave-one-dataset-out testing on the most commonly used benchmarks... In Table 4, we adopt the protocols proposed in (Huang et al. 2024a) to conduct cross-domain testing... In particular, the authors in (Huang et al. 2024a) proposed adopting the leave-one-attack-out strategy to consider 3D mask, print, and replay as the unseen attack type within six protocols. |
| Hardware Specification | No | The paper mentions using "pretrained contrastive language-image pretraining model (CLIP)" and discusses model size and inference speed, but does not provide specific details about the hardware (e.g., GPU models, CPU types) used for running experiments. |
| Software Dependencies | No | The paper mentions using the "pretrained contrastive language-image pretraining model (CLIP)" but does not specify version numbers for CLIP or any other programming languages, libraries, or solvers. |
| Experiment Setup | Yes | To train SLIP, we set a constant learning rate of 1e 5 with Adam optimizer up to 50 epochs. |