2D Masks Attack for facial recogniton system

The dataset consists of 4,800+ videos of people wearing of holding 2D printed masks filmed using 5 devices. It is designed for liveness detection algorithms, specifically aimed at enhancing anti-spoofing capabilities in biometric security systems. By leveraging this dataset, researchers can create more sophisticated recognition system, crucial for achieving iBeta Level 1 & 2 certification – a key standard for secure and reliable biometric… See the full description on the dataset page: https://huggingface.co/datasets/UniDataPro/printed-2d-masks-attacks.

The 3D Mask Attack Database (3DMAD) is a biometric (face) spoofing database. It contains 76500 frames of 17 persons, recorded using Kinect for both real-access and spoofing attacks. 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).

The data is collected in 3 different sessions for all subjects and for each session 5 videos of 300 frames are captured. The recordings are done under controlled conditions, with frontal-view and neutral expression. The first two sessions are dedicated to the real access samples, in which subjects are recorded with a time delay of ~2 weeks between the acquisitions. In the third session, 3D mask attacks are captured by a single operator (attacker).

In each video, the eye-positions are manually labelled for every 1st, 61st, 121st, 181st, 241st and 300th frames and they are linearly interpolated for the rest.

The real-size masks are obtained using "ThatsMyFace.com". The database additionally contains the face images used to generate these masks (1 frontal and 2 profiles) and paper-cut masks that are also produced by the same service and using the same images.

The satellite package which contains the Bob accessor methods to use this database directly from Python, with the certified protocols, is available in two different distribution formats:

You can download it from PyPI, or

You can download it in its source form from its git repository.

Acknowledgments

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

Nesli Erdogmus and Sébastien Marcel, "Spoofing in 2D Face Recognition with 3D Masks and Anti-spoofing with Kinect", Biometrics: Theory, Applications and Systems, 2013. 10.1109/BTAS.2013.6712688 https://publications.idiap.ch/index.php/publications/show/2657

2D Mask Attack Dataset - 26 436 videos

The dataset comprises 26,436 videos of real faces, 2D print attacks (printed photos), and replay attacks (faces displayed on screens), captured under varied conditions. Designed for attack detection research, it supports the development of robust face antispoofing and spoofing detection methods, critical for facial recognition security. Ideal for training models and refining anti-spoofing methods, the dataset enhances detection accuracy in… See the full description on the dataset page: https://huggingface.co/datasets/UniDataPro/2d-printed-mask-dataset.

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

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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

The dataset consists of selfies of people and videos of them wearing a printed 2d mask with their face. The dataset solves tasks in the field of anti-spoofing and it is useful for buisness and safety systems. The dataset includes: attacks - videos of people wearing printed portraits of themselves with cut-out eyes.

2D Masks with Eyeholes Attacks

The dataset comprises 11,200+ videos of people wearing of holding 2D printed masks with eyeholes captured using 5 different devices. This extensive collection is designed for research in presentation attacks, focusing on various detection methods, primarily aimed at meeting the requirements for iBeta Level 1 & 2 certification. Specifically engineered to challenge facial recognition and enhance spoofing detection techniques. By utilizing this dataset… See the full description on the dataset page: https://huggingface.co/datasets/UniDataPro/2d-masks-pad-attacks.

The dataset consists of videos of individuals wearing printed 2D masks or printed 2D masks with cut-out eyes and directly looking at the camera. Videos are filmed in different lightning conditions and in different places (indoors, outdoors). Each video in the dataset has an approximate duration of 2 seconds.

Discover the Presentation Attack Detection 2D Dataset, a comprehensive collection of videos capturing individuals wearing printed 2D masks.

The dataset consists of videos of individuals wearing printed 2D masks or printed 2D masks with cut-out eyes and directly looking at the camera. Videos are filmed in different lightning conditions and in different places (indoors, outdoors). Each video in the dataset has an approximate duration of 2 seconds.

Attacks with 2D Printed Masks of Indian People - Biometric Attack Dataset

The dataset consists of videos of individuals wearing printed 2D masks of different kinds and directly looking at the camera. Videos are filmed in different lightning conditions and in different places (indoors, outdoors). Each video in the dataset has an approximate duration of 3-4 seconds.

  💴 For Commercial Usage: Full version of the dataset includes 3394 videos, leave a request on TrainingData… See the full description on the dataset page: https://huggingface.co/datasets/UniqueData/attacks-with-2d-printed-masks-of-indian-people.

The dataset consists of photos of individuals and videos of him/her wearing printed 2D mask with cut-out holes for eyes. Videos are filmed in different lightning conditions and in different places (indoors, outdoors), a person moves his/her head left, right, up and down. Each video in the dataset has an approximate duration of 15-17 seconds.

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).

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.

Cut 2D Masks Presentation Attack Detection for Liveness Detection (2K+ individuals)

Liveness Detection Dataset with video attacks with printed 2D masks. This dataset focuses on cutout photo print attacks which might be used by iBeta and NIST FATE to assess liveness detection algorithms. This dataset is tailored for training AI models to identify a variation of cutout 2D print attack

  Full version of dataset is availible for commercial usage - leave a request on our… See the full description on the dataset page: https://huggingface.co/datasets/AxonData/Anti_Spoofing_Cut_print_attack.

Mobile 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 4 types of videos filmed on mobile devices:

2D mask with holes for eyes - demonstration of an attack with a paper/cardboard mask (mask) 2D mask with holes for eyes, nose, and mouth - demonstration… See the full description on the dataset page: https://huggingface.co/datasets/UniqueData/on-device-face-liveness-detection.

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.