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 | Archive PDF | Plain Text | 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. |