Face spoof dataset Face Anti-Spoofing dataset by Kolzek Therefore, the public set of the MSU USSA database for face spoofing consist of 9,000 images (1,000 live subject and 8,000 spoof attack) of the 1,000 subjects. Contribute to Xianhua-He/cvpr2024-face-anti-spoofing-challenge development by creating an account on GitHub. Jun 6, 2023 · Photo by Max Bender on Unsplash What to expect from this Blog: Some challenges of face anti-spoofing. Jul 24, 2020 · Among them, face anti-spoofing emerges as an important area, whose objective is to identify whether a presented face is live or spoof. With the successful deployments in phone unlock, access control and e-wallet payment, facial interaction systems already become an integral part in the real world. Jain and H. The dataset has two classes: real and spoof. The model evaluation is conducted online on the hidden test set. It consists of 8 videos of 35 subjects. The proposed framework presents a fresh approach to face anti-spoofing, leveraging Download scientific diagram | Samples from NUAA dataset [8] from publication: Face Spoof Detection Using VGG-Face Architecture | Face recognition systems have been obtaining substantial importance An ensemble classifier, consisting of multiple SVM classifiers trained for different face spoof attacks (e. 2) Lack of Annotations. Dec 11, 2022 · Abstract Without deploying face anti-spoofing countermeasures, face recognition systems can be spoofed by presenting a printed photo, a video, or a silicon mask of a genuine user. Essentially Mar 11, 2019 · Learn how to detect liveness with OpenCV, Deep Learning, and Keras. Abstract 3D mask face spoofing attack becomes new challenge and attracts more research interests in recent years. Anti Spoofing Real - Liveness Detection dataset The Biometric Attack dataset consists of 98,000 videos and selfies from people from 170 countries. Datasets HyperSpoof has been tested on multiple datasets to validate its effectiveness: RECOD-MPAD: Mobile-based presentation attack dataset. Among 43 rich attributes, 40 attributes belong to Live images including all facial components and The 3D Mask Attack Database (3DMAD) is a biometric (face) spoofing database. While the dataset itself doesn't contain spoofing attacks, it's a valuable resource for testing liveness detection system, allowing researchers to simulate attacks and evaluate To address these shortcomings, we introduce the Wild Face Anti-Spoofing (WFAS) dataset, a large-scale, diverse FAS dataset collected in unconstrained settings. Previous works have provided many databases for face anti-spoofing, but Hello everyone. This dataset was released in 2019 and became one of the key datasets created for multi-modal face anti-spoofing at the time. Due to IP issues, the public set we released is slightly different from the public set used in our paper (the genuine and spoof images of the subjects from the Idiap and CASIA databases CelebA-Spoof: Large-Scale Face Anti-Spoofing Dataset with Rich Annotations Yuanhan Zhang, Zhenfei Yin, Yidong Li, Guojun Yin, Junjie Yan, Jing Shao and Ziwei Liu In ECCV 2020. Oct 10, 2020 · This is the largest dataset for face spoofing prevention. In the practical application of the face anti-spoofing system, the classification boundary trained based on existing datasets may lead to overlapping characteristics between bona fide and novel attack sample data in certain domains, thereby impeding accurate classification. From phone unlocking, access control, to mobile payments, almost every facial interaction system faces the soul-searching question: "Is this face live or spoofed?" However, as methods continue to evolve one after another, a critical issue has quietly surfaced: The datasets are no @inproceedings{CelebA-Spoof, title={CelebA-Spoof: Large-Scale Face Anti-Spoofing Dataset with Rich Annotations}, author={Zhang, Yuanhan and Yin, Zhenfei and Li, Yidong and Yin, Guojun and Yan, Junjie and Shao, Jing and Liu, Ziwei}, booktitle={European Conference on Computer Vision (ECCV)}, year={2020} } Biometrics utilize physiological, such as fingerprint, face, and iris, or behavioral characteristics, such as typing rhythm and gait, to uniquely identify or authenticate an individual. The dataset used is the Large Crowdcollected Facial Anti-Spoofing Dataset, a well knowend dataset used for face anti-spoofing model training. 2. Jun 2, 2025 · 1 Introduction Face Anti-Spoofing (FAS) plays a vital role in securing face recognition systems, yet it must contend with a wide spectrum of sophisticated presentation attacks such as printed photos, screen-based replay, and 3D masks. We collect a comprehensive Near-Infrared face anti-spoofing Dataset, which incorporates six illumination conditions and comprises 380,000 images from 1,040 distinct identities. With the DeepFace library in Python, you can easily perform real-time analysis to detect and prevent spoof attacks, ensuring that only authorized individuals gain access. Photo,video,andthree-dimensionalfacemodelattacks are all common types of spoofing attacks. However, there exists a vital threat to these Biometrics utilize physiological, such as fingerprint, face, and iris, or behavioral characteristics, such as typing rhythm and gait, to uniquely identify or authenticate an individual. This project aims to provide a starting point in recognising real and fake faces based on a model that is trained with publicly available dataset SiW-Mv2 dataset contains 14 spoof types spanning from typical print and replay attack, to various masks, impersonation makeup and physical material coverings. [paper] | [video] Abstract: CelebA-Spoof is a large-scale face anti-spoofing dataset that has 625,537 images from 10,177 subjects, which includes 43 rich attributes on face, illumination,environment and spoof types. 👏 Survey of Deep Face Anti-spoofing 🔥 This is the official repository of "Deep Learning for Face Anti-Spoofing: A Survey", a comprehensive survey of recent progress in deep learning methods for face anti-spoofing (FAS) as well as the datasets and protocols. However, compared to the large existing image classification [14] and face recognition [51] datasets, face anti-spoofing datasets have less than 170 subjects and 60; 00 video clips, see Table 1. You'll learn how to detect fake faces and perform anti-face spoofing in face recognition systems with OpenCV. Different spoofing methods and related public datasets. Jul 1, 2024 · To address the limitations of existing Near-Infrared Dataset in the diversity of identities acquisition environment and acquisition devices. Live images are selected from the CelebA dataset. Jul 1, 2023 · FAQs: Q: How does VGG-Face architecture contribute to face spoofing detection? The VGG-Face architecture utilizes deep convolutional neural networks (CNNs) trained on large-scale datasets to extract discriminative features from faces. Face Antispoofing dataset for recognition systems The dataset consists of 98,000 videos and selfies from 170 countries, providing a foundation for developing robust security systems and facial recognition algorithms. We collected a face anti-spoofing experimental dataset with depth information, and reported extensive experimental results to validate the robustness of the proposed method. Jun 1, 2025 · The above system is a light-weight architecture mainly devised for the low-memory devices and tested on the publicly available standard datasets created in closed conditions, researchers can create custom real time datasets for face spoofing detection and test it with such light-weight architecture for their performance evaluation. Wen, A. In this work, we aimed to develop a novel Face Anti-Spoofing Identifier Network (FASIN) with Depth and Near Infrared (NIR) embeddings, trained on multi modalities using multi MSU Mobile Face Spoofing Database The MSU Mobile Face Spoofing Database (MFSD) is a benchmark dataset for face anti-spoofing. Live To overcome these obstacles, we contribute a large-scale face anti-spoo ng dataset, CelebA-Spoof, with the following appealing properties: 1) Quantity: CelebA-Spoof comprises of 625,537 pictures of 10,177 subjects, signi cantly larger than the existing datasets. Convolution Neural Networks (CNNs), in particular, are deep learning models that have shown impressive results recently in 😎 face anti-spoofing releated algorithm, dataset and paper - Elroborn/awesome-face-anti-spoofing Oct 1, 2020 · Request PDF | CelebA-Spoof: Large-Scale Face Anti-spoofing Dataset with Rich Annotations | As facial interaction systems are prevalently deployed, security and reliability of these systems become SiW-Mv2 dataset contains 14 spoof types spanning from typical print and replay attack, to various masks, impersonation makeup and physical material coverings. Real-Time Face Liveness Detection and Face Anti-spoofing Using Deep Learning. ) and related resources. Created by Project Abstract: CelebA-Spoof is a large-scale face anti-spoofing dataset that has 625,537 images from 10,177 subjects, which includes 43 rich attributes on face, illumination,environment and spoof types. Contribute to kprokofi/light-weight-face-anti-spoofing development by creating an account on GitHub. These videos include different facial motions collected from 20 subjects. To overcome these obstacles, we contribute a large-scale face anti-spoofing dataset, CelebA-Spoof, with the following appealing properties: 1) Quantity: CelebA-Spoof comprises of 625,537 pictures of 10,177 subjects, significantly larger than A curated list of Face Authentication Security (including face anti-spoofing/face presentation attack/face liveness detection, face attack models, etc. 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. The paper also examines the impact of annotation information on face spoofing prevention using AENet, a multitasking framework, and provides a benchmark. For example, an intruder might use a photo of the legal user to "deceive" the face recognition system. We train a deep Siamese network with image pairs. Jiang et al. The proposed approach is extended to multi-frame face spoof detection in videos using a voting based scheme. LCC FASD: Focused on face anti-spoofing. Current methods, trained on existing fake faces, of-ten lack generalization and perform poorly against unseen attacks. Meanwhile, most of existing databases only concentrate on the anti-spoofing of different kinds of attacks and ignore the environmental changes in real world Aug 13, 2020 · CelebA-Spoof is an anti-spoofing dataset that consists of 625,537 images of 10,177 people. CelebA-Spoof: Large-Scale Face Anti-Spoofing Abstract Face anti-spoofing (FAS) is an essential mechanism for safeguarding the integrity of automated face recognition systems. For example, obfuscation makeup and Continue reading Keywords: Face antispoofing A machine learning-powered face liveness detection system designed to distinguish between real faces and spoof attacks (like photos, videos, and masks) using deep learning. Recently, face PAD algorithms [20, 32] have achieved great performances. Database stats SiW provides live and spoof videos from 165 Aug 1, 2014 · The publically available MSU MFSD Database for face spoof attack consists of 280 video clips of photo and video attack attempts to 35 clients. The trained model is later tested with FastAPI. Accepted by CVPR Workshop 2024. Thus, face presen Facial anti-spoofing is the task of preventing false facial verification by using a photo, video, mask or a different substitute for an authorized person’s face. It acts as an important step to select the face image to the face recognition system. The Replay attack dataset wont accept students So I'd Nov 1, 2025 · The field of face anti-spoofing (FAS) has witnessed great progress with the surge of deep learning. This study delves into diverse aspects of dataset preprocessing to refine face spoof detection. ). This paper presents an extended version of the dataset aimed at addressing ethnic bias, named CASIA-SURF CeFA. This is inspired by Awesome-deep-vision, Awesome-adversarial-machine-learning, Awesome-deep-learning-papers, Awesome-NAS and Awesome-Pruing Please feel free to pull requests or open an issue to add papers. Han: "Face Spoof Detection with Image Distortion Analysis", In IEEE Trans. Each image pair consists of two real face images or one real and one spoof face image. One, random forest, uses the identified by us seven no-reference image quality features derived ABSTRACT Face anti-spoofing (FAS) is a crucial task in the field of face recognition practices, which aims to detect and prevent attempts to spoof/attack a facial recognition system using fake or manipulated images. However, face recognition is vulnerable to spoofing attacks which limits its usage in sensitive application areas. Introduction: SiW-Mv2 Dataset is a large-scale face anti-spoofing (FAS) dataset that is first introduced in the multi-domain FAS updating algorithm. With the advance of sensor technology, existing anti-spoofing systems can be vulnerable to emerging high-quality spoof mediums. The video frames are extracted, face localization is performed using Viola Jones Harr Cascade, and sample faces are randomly split into training, validation and testing sets. This work introduces a novel face anti-spoofing system, FASS, that fuses results of two classifiers. While face anti-spoofing tech-niques have received much attention to aim at identifying whether the captured face is genuine or fake, most face-spoofing detection techniques are biased towards a specific presentation attack type or presentation device; failing to robustly detects various spoofing scenarios. CelebA-Spoof: Large-scale dataset with rich annotations. Compared to other public domain face spoof databases, the MSU database has the following desirable properties: 1) Mobile phone is used to Jun 16, 2023 · Frequently Asked Questions Q: Are there any free face anti-spoofing repositories available on GitHub? A: Yes, several open-source repositories offer free implementations of face anti-spoofing algorithms. While the dataset itself doesn't contain spoofing attacks, it's a valuable resource for testing liveness detection system, allowing To facilitate face anti-spoofing research, we intro-duce a large-scale multi-modal dataset, namely CASIA-SURF, which is the largest publicly available dataset for face anti-spoofing in terms of both subjects and visual modalities. The classi cation perfor-mance on several face anti-spoo ng datasets has already saturated, failing to Apr 13, 2025 · Face Anti-Spoofing (FAS) has become a familiar term in the computer vision community over the past few years. Feb 25, 2021 · It is the largest face anti-spoofing dataset in terms of the numbers of the data and the subjects. CelebA-Spoof is a large-scale face anti-spoofing dataset that has 625,537 images from 10,177 subjects, which includes 43 rich attributes on face, illumination, environment and spoof types. As biometric systems are widely used in real-world applications including mobile phone authentication and access control, biometric spoof, or Presentation Attack (PA) are becoming a larger threat, where a Oct 7, 2020 · In this paper, we construct a large-scale face anti-spoofing dataset, CelebA-Spoof, with 625,537 images from 10,177 subjects, which includes 43 rich attributes on face, illumination, environment and spoof types. Pytorch implementation of Beyond the pixel world: A Novel Acoustic-based Face Anti-Spoofing System for Smartphones - ChenqiKONG/EchoFAS The main purpose of silent face anti-spoofing detection technology is to judge whether the face in front of the machine is real or fake. It contains over 625,000 images of 10,000 subjects, incorporating various spoofing attacks, including printed photos, replayed videos, and 3D masks. 627 open to forgery. In this pa-per, we propose a novel two-stream CNN-based approach for face anti-spoofing, by extracting Jul 1, 2024 · Face Anti-Spoofing (FAS) seeks to protect face recognition systems from spoofing attacks, which is applied extensively in scenarios such as access control, electronic payment, and security surveillance systems. Face anti-spoofing is a method of combating the cheating of face recognition systems. Dec 25, 2024 · Face anti-spoofing is currently a critical aspect in face biometric systems to enhance security by distinguishing between real and spoof images. Though promising progress has been achieved, existing works still have difficulty in handling complex spoof attacks and generalizing to real-world scenarios. , small scale, insignificant variance, etc. This is the code repository release for the paper "Assessing the Performance of Efficient Face Anti-Spoofing Detection Against Physical and Digital Presentation Attacks" accepted at the 5th Face Anti-Spoofing Workshop and Challenge@CVPR2024. A series of face anti-spoofing datasets, for the convenience of management and benchmarking. Hadid, "Face anti-spoofing based on color texture analysis", IEEE International Conference in Image Processing (ICIP), Quebec City, 2015, pp. Convolution Neural Networks (CNNs), in particular, are deep learning models that have shown impressive results recently in 😎 face anti-spoofing releated algorithm, dataset and paper - Elroborn/awesome-face-anti-spoofing towards the solving spoofing problem. Face anti-spoofing is an important task in full-stack face applications including face detection, verification, and recognition. CelebA-Spoof dataset contains 625,537 images from 10,177 subjects covering 43 attributes such as the face, illumination, environment, and spoof. Oct 10, 2020 · 3 main points ️ Proposed "CelebA-Spoof", a large dataset for face impersonation prevention containing 43 rich attribute information ️ The multitasking framework AENet was used to examine the impact of attribute information on the task of preventing face spoofing. This project runs in real-time using a webcam and is optimized for browser-based applications with TensorFlow. 30,000+ videos of attacks with masks filmed on a web camera - anti-spoofing The ten face images are classified separately and the average of the resulting scores is used as a final score for the whole video sequence. This Database was produced at the Michigan State University Pattern Recognition and Image Processing (PRIP). Abstract: CelebA-Spoof is a large-scale face anti-spoofing dataset that has 625,537 images from 10,177 subjects, which includes 43 rich attributes on face, illumination,environment and spoof types. 8269 open source Faces images plus a pre-trained Face-Anti-Spoofing model and API. The face presented by other media can be defined as false face, including printed paper photos, display screen of electronic products, silicone mask, 3D human image, etc. (link) November 2020, face anti-spoofing paper [4] is accepted by WACV 2021, where we release the CASIA-SURF CeFA dataset. Dec 1, 2019 · In this study, we introduce a new face anti-spoofing dataset named as CIGIT-PPM, which includes paired visible light and near-infrared images with spoofing medium, distance, pose, expression and session variations for both print and 3D mask attacks. We collect and annotate spoof images for CelebA-Spoof. Abstract In this paper, we examine how pre-processing and training methods impact on the performance of Lightweight CNNs through evaluations on With the advance of sensor technology, existing anti-spoofing systems can be vulnerable to emerging high-quality spoof mediums. This study evaluates the performance of three vision-based models, MobileNetV2, ResNET50, and Vision Transformer (ViT), for spoof detection in image classification, utilizing a dataset of 150,986 images divided into training (140,002 The acquisition devices were a laptop and an Android phone. MSU-MFSD Face presentation attack detection using video-based methods that analyze facial motion in successive video frames. Chronicle of FAS methodology using CelebA-Spoof is a large-scale face anti-spoofing dataset that has 625,537 images Kaggle is the world’s largest data science community with powerful tools and resources to help you achieve your data science goals. 2636-2640. Existing datasets have only annotated the type of spoof type. , printed photo and replayed video), is used to distinguish between genuine and spoof faces. It contains 76500 frames of 17 persons, recorded using Kinect for both real-access and spoofing attacks. But in this repository, we will only use the second one. Anti-Spoofing dataset: live, replay, cut, print, 3D masks - large-scale face anti spoofing This dataset delivers a single, end-to-end resource for training and benchmarking facial liveness-detection systems. Due to its data-driven nature, existing FAS method… Oct 11, 2021 · To promote the development of face anti-spoofing detection algorithms for silicone mask attacks, this paper constructs a Silicone Mask Face Motion Video Dataset (SMFMVD) containing 200 real face videos and 200 silicone mask face videos. However, existing face anti-spoofing benchmarks have limited number of subjects ( 170) and modalities ( 2), which hinder the further development of the academic community. To April 19, 2021, the 3rd Face Anti-spoofing challenge@ICCV2021 is begining. Boulkenafet, J. This limitation can be attributed to the scarcity and lack of diversity in publicly available FAS datasets, which often leads to overfitting Acknowledgments Liveness detection with OpenCV tutorial by Adrian Rosebrock. Previous approaches build models on datasets which do not simulate the real-world data well (e. Jun 8, 2024 · In conclusion, implementing face anti-spoofing techniques is crucial for enhancing the security of facial recognition systems. Feb 12, 2025 · These features are fused using the Concatenate function to form a more comprehensive representation for im-proved classification. Information Forensic and Security, 2015. The CVPR Face Anti-Spoofing Challenge provided comprehensive datasets covering various attack types an-nually, pushing the boundaries of spoof detection in terms of accuracy and generalizability using standard metrics like APCER and BPCER. (link) April 2021, the proposal of the 3rd Face Anti-spoofing Workshop and Challenge@ICCV 2021 is accepted. Full version of dataset is availible for commercial usage - leave a request on our website Axon Labs to purchase the dataset 💰 Our 3D Mannequin Anti-Spoofing Dataset provides a comprehensive collection of mannequin images, optimized for enhancing liveness detection models in face anti-spoofing. CASIA-SURF: Multi-modal benchmark dataset. g. Expedients to deceive these systems are getting more complicated, and antidotemethodsarenecessary. ️Three generic benchmarks were proposed to support a comprehensive assessment. Komulainen, and A. This paper reports methods and results in the CelebA-Spoof Challenge 2020 on Face AntiSpoofing which employs the CelebA-Spoof dataset. K. The dataset was collected with the intention to build a large and diverse dataset for anti-spoofing considering the current limitations (in both size and diversity) of existing datasets for this purpose. It addresses the shortage of high-quality text-image multimodal data by aggregating 1. We collect and annotate spoof images of CelebA-Spoof. With the emergence of large-scale academic datasets in the Abstract—Face anti-spoofing is essential to prevent face recog-nition systems from a security breach. [1] Z. One way to make the system robust to these attacks is to collect new high-quality databases. We used its training data large of 8299 images and divided it into train val and test with the following ratio, 80% 10% and 10%. The videos were gathered by capturing faces of genuine individuals presenting spoofs, using facial presentations. The data is collected Mar 1, 2023 · In our method, face spoofing is detected after face recognition rather than before face recognition; that is, the input face is recognized first, and the client identity is used to assist face spoofing detection. Much of the progresses have been made by the availability of face anti-spoofing bench-mark datasets in recent years. The diversity of attack types poses significant challenges to FAS models. The dataset can be downloaded via Google Drive (please share your name, affiliation, and Dec 28, 2021 · Face anti-spoofing systems has lately attracted increasing attention due to its important role in securing face recognition systems from fraudulent attacks. Meanwhile, Multimodal Large Language Models (MLLMs) have recently achieved breakthroughs in image-text understanding and semantic reasoning Abstract The face image is the most accessible biometric modality which is used for highly accurate face recognition systems, while it is vulnerable to many different types of presentation attacks. (2019) introduced a new face anti-spoofing dataset named CIGIT-PPM, which includes paired visible light Mar 25, 2025 · Digital image spoofing has emerged as a significant security threat in biometric authentication systems, particularly those relying on facial recognition. ABSTRACT The study about the vulnerabilities of biometric systems against spoofing has been a very active field of research in recent years. Existing models may rely on auxiliary information, which prevents these anti-spoofing solutions from generalizing Mar 29, 2025 · While past research has applied machine learning and deep learning techniques for spoof detection, challenges persist in image preprocessing and feature extraction, which are essential for enhancing the effectiveness of face anti-spoofing models. The face anti-spoofing is an technique that could prevent face-spoofing attack. Apr 14, 2023 · Face recognition technology has been widely used due to the convenience it provides. The limited Jun 19, 2024 · The CelebA-Spoof [36] dataset is a big dataset created especially for face anti-spoofing tasks. 3) Performance Saturation. We have noticed that currently most of face replay anti-spoofing databases focus on data with little variations of the devices used for replay and Face anti-spoofing is an important task in computer vision, which aims to facilitate facial interaction systems to determine whether a presented face is live or spoof. Live image selected from the CelebA dataset. Face anti-spoofing is a very critical step before feeding the face image to biometric systems. Despite substantial advancements, the general-ization of existing approaches to real-world applications remains challenging. For this project, we are developing a face anti spoofing system with a pretrained yolov8 model. Abstract Face anti-spoofing (FAS) has lately attracted increasing attention due to its vital role in securing face recognition systems from presentation attacks (PAs). The ROSE-Youtu Face Liveness Detection Database (ROSE-Youtu) consists of 4225 videos with 25 subjects in total (3350 videos with 20 subjects publically Mar 16, 2024 · To facilitate face anti-spoofing research, we introduce a large-scale multi-modal dataset, namely CASIA-SURF, which is the largest publicly available dataset for face anti-spoofing in terms of both subjects and visual modalities. 2019. - liuajian/CASIA-FAS-Dataset ROSE-Youtu Face Liveness Detection Dataset We introduce a new and comprehensive face anti-spoofing database, ROSE-Youtu Face Liveness Detection Database, which covers a large variety of illumination conditions, camera models, and attack types. However, there exists a vital threat to these face interaction Photo Print Attacks Dataset: 1K Individuals Face Anti-Spoofing Liveness dataset videos with Zoom in effect, High-Res Print Share with us your feedback and recieve additional samples for free!😊 Full version of dataset is availible for commercial usage - leave a request on our website Axon Labs to purchase the dataset 💰 Photo Print attack dataset (1K individuals+) for Presentation Attack May 15, 2025 · FaceCoT is the first Visual Question Answering (VQA) dataset tailored for Face Anti-Spoofing (FAS). In this particular research we are focusing on one of the most difficult types of attack - video replay. The proposed method is evaluated on two widely used face spoofing datasets, NUAA Photograph Imposter and LCC-FASD, achieving 100% accuracy on NUAA and 99% on LCC-FASD. If you want to use data from this repo, please, cite the authors of the original dataset: D. In response to this need, we collect a new face anti-spoofing database named Spoof in the Wild (SiW) database. As more and more realistic PAs with novel types spring up, early-stage FAS methods based on handcrafted features become unreliable due to their limited representation capacity. Nov 1, 2025 · The field of face anti-spoofing (FAS) has witnessed great progress with the surge of deep learning. As biometric systems are widely used in real-world applications including mobile phone authentication and access control, biometric spoof, or Presentation Attack (PA) are becoming a larger threat, where a Mar 25, 2025 · Digital image spoofing has emerged as a significant security threat in biometric authentication systems, particularly those relying on facial recognition. Face anti-spoofing task solution using CASIA-SURF CeFA dataset, FeatherNets and Face Alignment in Full Pose Range Nov 2, 2024 · 2. The fake or spoof faces are made from high quality records of the genuine/real 1 Introduction Face anti-spoofing is an important task in computer vision, which aims to facil-itate facial interaction systems to determine whether a presented face is live or spoof. Each frame consists of: a depth image (640x480 pixels – 1x11 bits) the corresponding RGB image (640x480 pixels – 3x8 bits) manually annotated eye positions (with respect to the RGB image). These solutions are crucial as newer devices, such as phones, have become vulnerable to spoofing attacks due to the availability of Aug 23, 2020 · The main reason is that current face anti-spoofing datasets are limited in both quantity and diversity. Face anti-spoofing requires the integration of local details and global semantic information. Aug 23, 2019 · Face anti-spoofing plays an important role in face recognition system to prevent security vulnerability. Besides the standard Spoof Type annotation, CelebA-Spoof also contains annotations for Illumination Condition and Environment, which express more information in face anti-spoo ng, compared to categorical label like Live/Spoof. This study evaluates the performance of three vision-based models, MobileNetV2, ResNET50, and Vision Transformer (ViT), for spoof detection in image classification, utilizing a dataset of 150,986 images divided into training (140,002 The CelebA-Spoof [36] dataset is a big dataset created especially for face anti-spoofing tasks. Our dataset proposes a novel approach that learns and detects spoofing techniques, extracting features from the genuine facial images Oct 3, 2023 · NIST_FRVT Top 1🏆 Face Recognition, Liveness Detection(Face Anti-Spoof), Face Attribute Analysis Linux Server SDK Demo ☑️ Face Recognition ☑️ Face Matching ☑️ Face Liveness Detection ☑️ Face Identification (1:N Face Search) ☑️ Face Attribute Analysis 1120 open source real-face-fake-face images. Our dataset proposes a novel approach that learns and detects spoofing techniques, extracting features from the genuine facial images to prevent the capturing of such information by fake users. The dataset provides data to combine and apply different techniques, approaches, and models to address the challenging task of distinguishing between genuine and spoofed inputs, providing effective anti-spoofing solutions in active authentication systems. Nov 1, 2017 · A face in front of the camera is classified as live if it is categorized as live using both cues. towards the solving spoofing problem. FaceCoT adopts a hierarchical Chain-of-Thought (CoT) annotation format that enhances interpretability and reasoning Abstract Face anti-spoofing is an important task in full-stack face applications including face detection, verification, and recognition. In this paper we propose a new anti-spoofing network architecture that takes advantage of multi-modal image data and aggregates intra-channel features at multiple network layers. The SiW-Mv2 dataset includes 14 spoof attack types, and these spoof attack types are designated and verified by the IARPA ODIN program. Deep learning-based face spoofing attack detection methods Several researchers have begun to suggest various Deep Neural Networks (DNNs) to address the issue of face anti-spoofing because of the deep neural network’s notable performance in the field of computer vision. This enables accurate identification of genuine faces and helps detect anomalies associated with spoofing attempts. To address these shortcomings in existing face anti-spoo ng dataset, in this work we propose a large-scale and densely annotated dataset, CelebA-Spoof. How-ever, due to the deficiency number and limited variations of database, there are few methods be proposed to aim on it. js. Face anti-spoof community lacks a densely annotated dataset covering rich attributes, which can further help researchers to explore face anti-spoo ng task with diverse attributes. I'm looking for these datasets: CASIA Face Anti-Spoofing Database Replay-Attack dataset: MSU Mobile Face Spoofing Database (MSU MFSD) Does anyone know where I can get these datasets? specially the Casia Face Anti-Spoofing Database? It seems the Casias link is dead and they do not respond to submission requests either. These repositories provide a great starting point for developers looking to explore and experiment with different approaches. For example, obfuscation makeup and partial coverings intend to hide subject’s identity The dataset consists of 98,000 videos and selfies from 170 countries, providing a foundation for developing robust security systems and facial recognition algorithms. 08M carefully annotated samples from WFAS and CelebA-Spoof, covering 14 distinct attack types. . With the emergence of large-scale academic datasets Jun 2, 2025 · Face Anti-Spoofing (FAS) typically depends on a single visual modality when defending against presentation attacks such as print attacks, screen replays, and 3D masks, resulting in limited generalization across devices, environments, and attack types. PDF Face anti-spoofing (FAS) has lately attracted increasing attention due to its vital role in securing face recognition systems from presentation attacks (PAs). Face anti-spoofing open dataset by Timoshenko, et al. Detecting spoofing attacks plays a vital role for deploying automatic face recognition for biometric authentication in applications such as access control, face payment, device unlock, etc. The SynthASpoof is the synthetic-based face presentation attack detection datasets, including synthetic-generated 25,000 bona fide images and 78,800 corresponding attacks collected by presenting the printed/replayed images to capture cameras (one mobile phone, two different tablets, and one webcam). We also transfer strong Dec 25, 2024 · Face anti-spoofing is currently a critical aspect in face biometric systems to enhance security by distinguishing between real and spoof images. One of the key points of this success is the availability of face anti-spoofing datasets [5, 7, 10, 32, 48, 53]. Abstract Introducing a novel Attack-agnostic Face Anti-spoofing framework, this paper addresses the challenge of determin-ing the authenticity of a captured face in face recognition systems. Biometric Attack dataset for the anti-spoofing task Aug 13, 2025 · Face Antispoofing dataset for liveness detection Anti-Spoofing dataset: live, replay, cut, print, 3D masks - large-scale face anti spoofing This dataset delivers a single, end-to-end resource for training and benchmarking facial liveness-detection systems. SiW-Mv2 has the largest variance in terms of the spoof pattern, each of these patterns are designated and verified by the IARPA project. Jul 28, 2020 · The novel dataset, named CelebA-Spoof is a large scale dataset containing more than 625 000 images of more than 10 000 subject participants.