The Custom Silicone Mask Attack Dataset (CSMAD) contains presentation attacks made of six custom-made silicone masks. Each mask cost about USD 4000. The dataset is designed for face presentation attack detection experiments.

The Custom Silicone Mask Attack Dataset (CSMAD) has been collected at the Idiap Research Institute. It is intended for face presentation attack detection experiments, where the presentation attacks have been mounted using a custom-made silicone mask of the person (or identity) being attacked.

The dataset contains videos of face-presentations, as a set of files specifying the experimental protocol corresponding the experiments presented in the corresponding publication.

Reference

If you publish results using this dataset, please cite the following publication.

Sushil Bhattacharjee, Amir Mohammadi and Sebastien Marcel: "Spoofing Deep Face Recognition With Custom Silicone Masks." in Proceedings of International Conference on Biometrics: Theory, Applications, and Systems (BTAS), 2018. 10.1109/BTAS.2018.8698550 http://publications.idiap.ch/index.php/publications/show/3887

Data Collection

Face-biometric data has been collected from 14 subjects to create this dataset. Subjects participating in this data-collection have played three roles: targets, attackers, and bona-fide clients. The subjects represented in the dataset are referred to here with letter-codes: A .. N. The subjects A..F have also been targets. That is, face-data for these six subjects has been used to construct their corresponding flexible masks (made of silicone). These masks have been made by Nimba Creations Ltd., a special effects company.

Bona fide presentations have been recorded for all subjects A..N. Attack presentations (presentations where the subject wears one of 6 masks) have been recorded for all six targets, made by different subjects. That is, each target has been attacked several times, each time by a different attacker wearing the mask in question. This is one way of increasing the variability in the dataset. Another way we have augmented the variability of the dataset is by capturing presentations under different illumination conditions. Presentations have been captured in four different lighting conditions:

flourescent ceiling light only

halogen lamp illuminating from the left of the subject only

halogen lamp illuminating from the right only

both halogen lamps illuminating from both sides simultaneously

All presentations have been captured with a green uniform background. See the paper mentioned above for more details of the data-collection process.

Dataset Structure

The dataset is organized in three subdirectories: ‘attack’, ‘bonafide’, ‘protocols’. The two directories: ‘attack’ and ‘bonafide’ contain presentation-videos and still images for attacks and bona fide presentations, respectively. The folder ‘protocols’ contains text files specifying the experimental protocol for vulnerability analysis of face-recognition (FR) systems.

The number of data-files per category are as follows:

‘bonafide’: 87 videos, and 17 still images (in .JPG format). The still images are frontal face images captured using a Nikon Coolpix digital camera.

‘attack’: 159, organized in two sub-folders – ‘WEAR’ (108 videos), and ‘STAND’ (51 videos)

The folder ‘attack/WEAR’ contains videos where the attack has been made by a person (attacker) wearing the mask of the target being attacked. The ‘attack/STAND’ folder contains videos where the attack has been made using a the target’s mask mounted on an appropriate stand.

Video File Format

The video files for the face-presentations are in ‘hdf5’ format (with file-extensions ‘.h5’. The folder structure of the hdf5 file is shown in Figure 1. Each file contains data collected using two cameras:

RealSense SR300 (from Intel): collects images/videos in visible-light (RGB color) , near infrared (NIR) @ 860nm wavelength, and depth maps

Compact Pro (from Seek Thermal): collects thermal (long-wave infrared (LWIR)) images.

As shown in Figure 1, frames from the different channels (color, infrared, depth, thermal) from he two cameras are stored in separate directory-hierarchies in the hdf5 file. Each file respresents a video of approximately 10 seconds, or roughly, 300 frames.

In the hdf5 file, the directory for SR300 also contains a subdirectory named ‘aligned_color_to_depth’. This folder contains post-processed data, where the frames of depth channel have been aligned with those of the color channel based on the time-stamps of the frames.

Experimental Protocol

The ‘protocols’ folder contains text files that specify the protocols for vulnerability analysis experiments reported in the paper mentioned above. Please see the README file in the protocols folder for details.

Silicone Mask Attack dataset

The dataset contains 6,500+ videos of attacks from 50 different people, filmed using 5 devices, providing a valuable resource for researching presentation attacks in facial recognition technologies. By focusing on this area, the dataset facilitates experiments designed to improve biometric security and anti-spoofing measures, ultimately aiding in the creation of more robust and reliable authentication systems. By utilizing this dataset, researchers can… See the full description on the dataset page: https://huggingface.co/datasets/UniDataPro/silicone-mask-attack.

This dataset consists of face & silicon masks images from 8 different subjects captured with 3 different smartphones.

This dataset consists of images captured from 8 different bona fide subjects using three different smartphones (iPhone X, Samsung S7 and Samsung S8). For each subject within the database, varying number of samples are collected using all the three phones. Similarly, the silicone masks of each of the subject is collected using three phones. The masks, each costing about USD 4000, have been manufactured by a professional special-effects company.

For the bona fide presentations of the same eight subjects, each data subject is asked to pose in a manner compliant to standard portrait capture. The data is captured indoors, with adequate artificial lighting. Silicone mask presentations have been captured under similar conditions, by placing the masks on their bespoke support provided by the manufacturer, with prosthetic eyes and silicone eye sockets.

The database is organized in three folders corresponding to three smartphones and further each subject within the database is organized in sub-folders.

The files are named using the convention "PHONE/CLASS/SUBJECTNUMBER/PHONEIDENTIFIER-PRESENTATION-SUBJECTNUMBER-SAMPLENUMBER.jpg".

• PHONE is iPhone, SamS7 or SamS8 corresponding to iPhone, Samsung S7 and Samsung S8 respectively.
• CLASS is "Bona" or "Mask" indicating the bona fide presentation or mask presentation respectively.
• SUBJECTNUMBER is "s1" to "s8" indicating 8 subjects in the database.
• PHONEIDENTIFIER is the two letter keyword as given by "ip", "s7" and "s8" corresponding to iPhone, Samsung S7 and Samsung S7 respectively.
• PRESENTATION identifies bona-fide or mask-attack presentation using 2 letter identifier "bp" or "ap".
• SAMPLENUMBER indicates the sample number of the subject.

Reference

If you publish results using this dataset, please cite the following publication.

“Custom Silicone Face Masks - Vulnerability of Commercial Face Recognition Systems & Presentation Attack Detection”, R. Raghavendra, S. Venkatesh, K. B. Raja, S. Bhattacharjee, P. Wasnik, S. Marcel, and C. Busch. IAPR/IEEE International Workshop on Biometrics and Forensics (IWBF), 2019.
10.1109/IWBF.2019.8739236
https://publications.idiap.ch/index.php/publications/show/4065

Silicone Mask Attack Dataset - 6,500+ videos

Dataset comprises 6,500+ videos of individuals wearing silicone masks, captured using 5 different devices. It is designed for research in presentation attacks, focusing on 3D masks, spoofing detection, and facial recognition challenges, particularly for achieving iBeta Level 2 certification. - Get the data

  Dataset characteristics:

Characteristic Data

Description Videos of people in silicone masks training… See the full description on the dataset page: https://huggingface.co/datasets/ud-ibeta/Silicone-Mask-Dataset.

Description

The eXtended Custom Silicone Mask Attack Dataset (XCSMAD) consists of 535 short video recordings of both bona fide and presentation attacks (PA) from 72 subjects. The attacks have been created from custom silicone masks. Videos have been recorded in RGB (visual spectra), near infrared (NIR), and thermal (LWIR) channels.

A complete preprocessed data for the aforementioned videos and bona fide images (as a part of experiments related to vulnerability assessment) have been provided to facilitate reproducing experiments from the reference publication, as well as to conduct new experiments. The details of preprocessing can be found in the reference publication.

The implementation of all experiments described in the reference publication is available at https://gitlab.idiap.ch/bob/bob.paper.xcsmad_facepad

Experimental protocols

The reference publication considers two experimental protocols: grandtest and cross-validation (cv). For a frame-level evaluation, 50 frames from each video have been used in both protocols. For the grandtest protocol, videos were divided into train, dev, and eval groups. Each group consists of unique subset of clients. (The videos corresponding to any specific subjects in one group are a part of single group).

For cross-validation (cv) experiments, a 5-fold protocol has been devised. Videos from XCSMAD have been split into 5 folds with non-overlapping clients. Using these five partitions, 5 testprotocols (cv0, · · · , cv4) have been created such that in each protocol, four of the partitions are used for training, and the remaining one is used for evaluation.

Reference

If you use this dataset, please cite the following publication:

@article{Kotwal_TBIOM_2019,
  author = {Kotwal, Ketan and Bhattacharjee, Sushil and Marcel, S\'{e}bastien},
  title = {Multispectral Deep Embeddings As a Countermeasure To Custom Silicone Mask Presentation Attacks},
  journal = {IEEE Transactions on Biometrics, Behavior, and Identity Science},
  publisher = {{IEEE}},
  year = {2019},
}

The dataset consists of videos of individuals and attacks with printed 2D masks and silicone masks . Videos are filmed in different lightning conditions (in a dark room, daylight, light room and nightlight). Dataset includes videos of people with different attributes (glasses, mask, hat, hood, wigs and mustaches for men).

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. By aggregating live sessions and eleven realistic presentation-attack classes into one collection, it accelerates development toward iBeta Level 1/2 compliance and strengthens model robustness against the full spectrum of spoofing tactics

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F20109613%2F6432e95d7b7fef1d271457f172e11e0c%2FFrame%20103-3.png?generation=1753867895186569&alt=media" alt="">

Why Comprehensive Anti-Spoofing Data?

Modern certification pipelines demand proof that a system resists all common attack vectors—not just prints or replays. This dataset delivers those vectors in one place, allowing you to: - Benchmark a model’s true generalisation - Fine-tune against rare but high-impact threats (e.g., silicone or textile masks) - Streamline audits by demonstrating coverage of every ISO 30107-3 attack category

Dataset Features

• Dataset Size: ≈ 95 000 videos / image sequences spanning live captures and eleven spoof classes
• Attack Diversity: 3D paper mask, wrapped 3D mask, photo print, mobile replay, display replay, cut-out 2D mask, silicone mask, latex mask, textile mask
• Active Liveness Cues: Natural blinks, and head rotations included across live and mask sessions
• Attribute Range: different combinations of hairstyles, eyewear, facial hair, and accessories.
• Environmental Variability: Indoor/outdoor scenes under various lighting conditions
• Multi-angle Capture: Mainly used selfie camera, also back
• Capture Devices: Footage from flagship and mid-range phones (iPhone 14 / 13 Pro, Galaxy S23, Pixel 7, Redmi Note 12 Pro+, Galaxy A54, Honor 70)
• Additional Flexibility: Custom re-captures available on request

Full version of dataset is availible for commercial usage - leave a request on our website Axonlabs to purchase the dataset 💰

Technical Specifications

• File Format: MP4 for video, JPEG/PNG for still sequences; all compatible with mainstream ML frameworks
• Resolution & FPS: Up to 4K @ 60 fps; balanced presets included for rapid training

Best Uses

Ideal for companies pursuing or maintaining iBeta Level 1/2 certification, research groups exploring new PAD architectures, and vendors stress-testing production face-verification pipelines

Attack Classes

• Live / Genuine Natural faces with spontaneous movements across varied devices and lighting
• 3D Paper Mask Folded paper masks with protruding nose/forehead
• Wrapped 3D Print Rigid paper moulds reproducing head geometry
• Photo Print Glossy still photos at multiple angles—the classic 2D spoof
• Cylinder 3D Paper Mask A folded or cylindrical sheet of paper that simulates volume
• Mobile Replay Face videos played on phone screens; includes glare and auto-brightness shifts
• Display Replay Attacks via monitors, and laptops
• Cut-out 2D Mask Flat printed masks with eye/mouth holes plus active head motion
• On-actor Print / Cuts Paper elements (photos, cutouts) are glued directly onto the actor's face
• Silicone and Latex Masks High-detail silicone/latex overlays with blinking and subtle mimicry
• Cloth 3D Mask Elastic fabric masks hugging facial contours during movement
• High-Fidelity Resin Mask Hyperrealistic masks with detailed skin texture

Conclusion

This dataset’s scale, breadth of attack types, and real-world capture conditions make it indispensable for anyone building or evaluating biometric anti-spoofing solutions. Deploy it to harden your systems against today’s—and tomorrow’s—most sophisticated presentation attacks

Face mask dataset for facial recognition

This dataset contains over 11,100+ video recordings of people wearing latex masks, captured using 5 different devices.It is designed for liveness detection algorithms, specifically aimed at enhancing anti-spoofing capabilities in biometric security systems. By utilizing this dataset, researchers can develop more accurate facial recognition technologies, which is crucial for achieving the iBeta Level 2 certification, a benchmark for robust and… See the full description on the dataset page: https://huggingface.co/datasets/UniDataPro/latex-mask-attack.

Silicone Mask Biometric Attack Dataset for Liveness Detection

10,000+ videos of attacks with Silicone 3D Masks for iBeta 2. The Dataset is designed to address security challenges in liveness detection systems through 3D silicone mask attacks. These presentation attacks are key for testing Passive Liveness Detection systems crucial for iBeta Level 2 certification. This dataset significantly enhances the capabilities of liveness detection models

  Full version of dataset is… See the full description on the dataset page: https://huggingface.co/datasets/AxonData/iBeta_level_2_Silicone_masks.

Textile 3D Face Mask Attack Dataset

This Dataset is specifically designed to enhance Face Anti-Spoofing and Liveness Detection models by simulating Nylon Mask Attacks — an accessible alternative to expensive silicone and latex mask datasets. These attacks utilize thin elastic fabric masks worn like a balaclava, featuring printed facial images that conform to the wearer's head shape through textile elasticity. The dataset is particularly valuable for PAD model training and iBeta… See the full description on the dataset page: https://huggingface.co/datasets/AxonData/3d_cloth_face_mask_spoofing_dataset.

Description This dataset contains images of 5 subjects. The images have been captured using the Intel Realsense SR300 camera, and the Xenics Gobi thermal camera. The SR300 returns 3 kinds of data: color (RGB) images, near-infrared (NIR) images, and depth information. For four subjects (subject1 – subject4), images have been captured with both cameras under two conditions: 1with the face visible, and with the subject wearing a rigid (resin-coated) mask Each subject has used 3 sets of rigid masks (corresponding to three identities (‘id0’, ‘id1’, ‘id2’, not necessarily corresponding to the subjects in this dataset), with two masks (‘mask0’, ‘mask1’) per identity. For subject5, data has been captured using both cameras under two conditions: with the face visible with the subject wearing a flexible (silicone) mask resembling subject5. For each combination (subject, camera, condition), several seconds of video have been captured, and the video-frames have been stored in uncompressed form (in .png) format. All images in .png format have been captured at a resolution of 640x480 pixels. Reference If you use this database, please cite the following publication: Sushil Bhattacharjee and Sébastien Marcel, "What you can't see can help you -- extended-range imaging for 3Dmask presentation attack detection", BIOSIG2017. 10.23919/BIOSIG.2017.8053524 https://publications.idiap.ch/index.php/publications/show/3710

Latex Mask Dataset for Face Anti-Spoofing and Liveness Detection

Anti spoofing dataset with Latex 3D mask attacks (4 000 videos) for iBeta 2. The Biometric Attack Dataset offers a robust solution for enhancing security in liveness detection systems by simulating 3D latex mask attacks. This dataset is invaluable for assessing and fine-tuning Passive Liveness Detection models, an essential step toward achieving iBeta Level 2 certification. By integrating diverse realistic presentation… See the full description on the dataset page: https://huggingface.co/datasets/AxonData/Latex_Mask_dataset.

Silicone Mask Biometric Attack Dataset

Wrapped 3D Attacks Dataset

  Full version of dataset is availible for commercial usage - leave a request on our website Axon Labs to purchase the dataset 💰





  Introduction

This dataset is designed to enhance Liveness Detection models by simulating Wrapped 3D Attacks — a more advanced version of 3D Print Attacks, where facial prints include 3D elements and additional attributes. It is particularly useful for iBeta Level 2… See the full description on the dataset page: https://huggingface.co/datasets/AxonData/Wrapped_3D_Attacks.

Description

The in-Vehicle Face Presentation Attack Detection (VFPAD) dataset consists of 4046 bona-fide recordings from 40 subjects, and 1790 attack presentation videos from a total of 89 PAIs (presentation attack instruments). These presentations have been captured using an NIR camera (940 nm) placed on the steering wheel of the car, while NIR illuminators have been fixed on both front pillars (adjacent to the wind-shield) of the car. The bona-fide videos represent 24 male and 16 female subjects of various ethnicities. The PAI species used to construct this dataset include photo-prints, digital displays (for replay attacks), rigid 3D masks, and flexible 3D masks made of silicone.

Data Collection

The videos comprising this dataset represent bona-fide and attack presentations under a range of variations:

• Environmental variations: presentations have been recorded in four sessions, each under different environmental conditions (outdoor sunny; outdoor cloudy; indoor dimly-lit; and indoor brightly-lit)
• Different scenarios: bona-fide presentations for each subject have been captured with variety of appearances: with/without glasses, with/without hat, etc.
• Illumination variations: two illumination conditions have been used: ‘uniform’ (both NIR illuminators switched on), and ‘non-uniform’ (only the left NIR-illuminator switched on), and
• Pose variations: two poses (‘angles’) have been used: ‘front’: the subject looks ahead at the road; and ‘below’: subject looks straight into the camera.

Citation

If you use the dataset, please cite the following publication:

@article{IEEE_TBIOM_2021,
author = {Kotwal, Ketan and Bhattacharjee, Sushil and Abbet, Philip and Mostaani, Zohreh and Wei, Huang and Wenkang, Xu and Yaxi, Zhao and Marcel, S\'{e}bastien},
title = {Domain-Specific Adaptation of CNN for Detecting Face Presentation Attacks in NIR},
journal = {IEEE Transactions on Biometrics, Behavior, and Identity Science},
publisher = {{IEEE}},
year={2022},
volume={4},
number={1},
pages={135--147},
doi={10.1109/TBIOM.2022.3143569}
}

Web Camera Face Liveness Detection

The dataset consists of videos featuring individuals wearing various types of masks. Videos are recorded under different lighting conditions and with different attributes (glasses, masks, hats, hoods, wigs, and mustaches for men). In the dataset, there are 7 types of videos filmed on a web camera:

Silicone Mask - demonstration of a silicone mask attack (silicone) 2D mask with holes for eyes - demonstration of an attack with a paper/cardboard mask… See the full description on the dataset page: https://huggingface.co/datasets/UniqueData/web-camera-face-liveness-detection.

iBeta Level 2 PAD Anti-Spoofing (3D Masks) — Liveness Detection Training Dataset

Comprehensive biometric dataset for iBeta Level 2 liveness detection training and anti-spoofing research. Emphasis on 3D attacks (masks) and movements for Active liveness (zoom-in/zoom-out, micro-movements), high variability of devices and conditions, high diversity of subjects

  Spoofing Attack Types:

Silicone Mask Attacks Latex Mask Attacks Wrapped 3D Paper Mask Attacks Advanced Paper Mask… See the full description on the dataset page: https://huggingface.co/datasets/AxonData/iBeta-Level-2-Certification-Dataset.

Liveness Detection Dataset

Dataset comprises 32,300 videos featuring 6 types of attacks, including Real Person, 2D masks, 2D masks with eyeholes, latex masks, wrapped 3D masks, and silicone masks, ensuring 100% iBeta Level 2 certification compliance. Designed for rigorous biometric testing and liveness detection*.- Get the data

  Dataset characteristics:

Characteristic Data

Description Videos of people for training algorithms to detect attempts to hack… See the full description on the dataset page: https://huggingface.co/datasets/ud-ibeta/iBeta-Level-2-Certification-Dataset.