Wild Face Anti-Spoofing Challenge 2023: Benchmark and Results

• URL: http://arxiv.org/abs/2304.05753v3
• Date: Fri, 5 May 2023 01:28:33 GMT
• Title: Wild Face Anti-Spoofing Challenge 2023: Benchmark and Results
• Authors: Dong Wang, Jia Guo, Qiqi Shao, Haochi He, Zhian Chen, Chuanbao Xiao, Ajian Liu, Sergio Escalera, Hugo Jair Escalante, Zhen Lei, Jun Wan, Jiankang Deng
• Abstract summary: Face anti-spoofing (FAS) is an essential mechanism for safeguarding the integrity of automated face recognition systems. This limitation can be attributed to the scarcity and lack of diversity in publicly available FAS datasets. We introduce the Wild Face Anti-Spoofing dataset, a large-scale, diverse FAS dataset collected in unconstrained settings.
• Score: 73.98594459933008
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Face anti-spoofing (FAS) is an essential mechanism for safeguarding the integrity of automated face recognition systems. Despite substantial advancements, the generalization of existing approaches to real-world applications remains challenging. This limitation can be attributed to the scarcity and lack of diversity in publicly available FAS datasets, which often leads to overfitting during training or saturation during testing. In terms of quantity, the number of spoof subjects is a critical determinant. Most datasets comprise fewer than 2,000 subjects. With regard to diversity, the majority of datasets consist of spoof samples collected in controlled environments using repetitive, mechanical processes. This data collection methodology results in homogenized samples and a dearth of scenario diversity. To address these shortcomings, we introduce the Wild Face Anti-Spoofing (WFAS) dataset, a large-scale, diverse FAS dataset collected in unconstrained settings. Our dataset encompasses 853,729 images of 321,751 spoof subjects and 529,571 images of 148,169 live subjects, representing a substantial increase in quantity. Moreover, our dataset incorporates spoof data obtained from the internet, spanning a wide array of scenarios and various commercial sensors, including 17 presentation attacks (PAs) that encompass both 2D and 3D forms. This novel data collection strategy markedly enhances FAS data diversity. Leveraging the WFAS dataset and Protocol 1 (Known-Type), we host the Wild Face Anti-Spoofing Challenge at the CVPR2023 workshop. Additionally, we meticulously evaluate representative methods using Protocol 1 and Protocol 2 (Unknown-Type). Through an in-depth examination of the challenge outcomes and benchmark baselines, we provide insightful analyses and propose potential avenues for future research. The dataset is released under Insightface.

Related papers

• FFAA: Multimodal Large Language Model based Explainable Open-World Face Forgery Analysis Assistant [20.83276248513432]
  We introduce FFAA: Face Forgery Analysis Assistant, consisting of a fine-tuned Multimodal Large Language Model (MLLM) and Multi-answer Intelligent Decision System (MIDS) Our method not only provides user-friendly explainable results but also significantly boosts accuracy and robustness compared to previous methods.
  arXiv  Detail & Related papers  (2024-08-19T15:15:20Z)
• Privacy-preserving datasets by capturing feature distributions with Conditional VAEs [0.11999555634662634]
  Conditional Variational Autoencoders (CVAEs) trained on feature vectors extracted from large pre-trained vision foundation models. Our method notably outperforms traditional approaches in both medical and natural image domains. Results underscore the potential of generative models to significantly impact deep learning applications in data-scarce and privacy-sensitive environments.
  arXiv  Detail & Related papers  (2024-08-01T15:26:24Z)
• DailyDVS-200: A Comprehensive Benchmark Dataset for Event-Based Action Recognition [51.96660522869841]
  DailyDVS-200 is a benchmark dataset tailored for the event-based action recognition community. It covers 200 action categories across real-world scenarios, recorded by 47 participants, and comprises more than 22,000 event sequences. DailyDVS-200 is annotated with 14 attributes, ensuring a detailed characterization of the recorded actions.
  arXiv  Detail & Related papers  (2024-07-06T15:25:10Z)
• Unified Physical-Digital Attack Detection Challenge [70.67222784932528]
  Face Anti-Spoofing (FAS) is crucial to safeguard Face Recognition (FR) Systems. UniAttackData is the largest public dataset for Unified Attack Detection. We organized a Unified Physical-Digital Face Attack Detection Challenge to boost the research in Unified Attack Detections.
  arXiv  Detail & Related papers  (2024-04-09T11:00:11Z)
• Diffusion Deepfake [41.59597965760673]
  Recent progress in generative AI, primarily through diffusion models, presents significant challenges for real-world deepfake detection. The increased realism in image details, diverse content, and widespread accessibility to the general public complicates the identification of these sophisticated deepfakes. This paper introduces two extensive deepfake datasets generated by state-of-the-art diffusion models.
  arXiv  Detail & Related papers  (2024-04-02T02:17:50Z)
• SynthASpoof: Developing Face Presentation Attack Detection Based on Privacy-friendly Synthetic Data [6.218678900574128]
  This work presents the first synthetic-based face PAD dataset, named SynthASpoof, as a large-scale PAD development dataset. The experimental results demonstrate the feasibility of using SynthASpoof for the development of face PAD. We show the viability of using synthetic data as a supplement to enrich the diversity of limited authentic training data.
  arXiv  Detail & Related papers  (2023-03-05T12:35:58Z)
• ArtiFact: A Large-Scale Dataset with Artificial and Factual Images for Generalizable and Robust Synthetic Image Detection [0.3779860024918729]
  This paper assesses the generalizability and robustness of synthetic image detectors in the face of real-world impairments. A proposed multi-class classification scheme, combined with a filter stride reduction strategy addresses social platform impairments. The solution significantly outperforms other top teams by 8.34% on Test 1, 1.26% on Test 2, and 15.08% on Test 3 in the IEEE VIP Cup challenge at ICIP 2022, as measured by the accuracy metric.
  arXiv  Detail & Related papers  (2023-02-23T12:40:36Z)
• Surveillance Face Anti-spoofing [81.50018853811895]
  Face Anti-spoofing (FAS) is essential to secure face recognition systems from various physical attacks. We propose a Contrastive Quality-Invariance Learning (CQIL) network to alleviate the performance degradation caused by image quality. A large number of experiments verify the quality of the SuHiFiMask dataset and the superiority of the proposed CQIL.
  arXiv  Detail & Related papers  (2023-01-03T07:09:57Z)
• CelebA-Spoof: Large-Scale Face Anti-Spoofing Dataset with Rich Annotations [85.14435479181894]
  CelebA-Spoof is a large-scale face anti-spoofing dataset. It includes 625,537 pictures of 10,177 subjects, significantly larger than the existing datasets. It contains 10 spoof type annotations, as well as the 40 attribute annotations inherited from the original CelebA dataset.
  arXiv  Detail & Related papers  (2020-07-24T04:28:29Z)
• Face Anti-Spoofing with Human Material Perception [76.4844593082362]
  Face anti-spoofing (FAS) plays a vital role in securing the face recognition systems from presentation attacks. We rephrase face anti-spoofing as a material recognition problem and combine it with classical human material perception. We propose the Bilateral Convolutional Networks (BCN), which is able to capture intrinsic material-based patterns.
  arXiv  Detail & Related papers  (2020-07-04T18:25:53Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.