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001_Print attributes in photo
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001_Print attributes in photo
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102_Cut attributes in photo
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102_Cut attributes in photo
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203_Cut attributes external
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203_Cut attributes external
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304_Photo on actor, external attributes
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304_Photo on actor, external attributes
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405_Photo on actor, attributes in photo
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405_Photo on actor, attributes in photo
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506_Photo on actor, external attributes, eye cutouts
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506_Photo on actor, external attributes, eye cutouts
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607_Photo on actor, attributes in photo, eye cutouts
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607_Photo on actor, attributes in photo, eye cutouts
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Liveness Detection Dataset: iBeta level 2 advanced mask attacks (5 K videos)
Anti-Spoofing Paper Attacks iBeta 2 - 5,000 videos 4 different attack types, advanced paper attacks for Liveness Detection
Full version of dataset is availible for commercial usage - leave a request on our website Axonlabs to purchase the dataset π°
Types of Presentation Attacks (paper masks)
- 1. Printed attributes on photo β a flat facial photo with accessories (e.g., glasses, hat) printed together with the face.
- 2. Cut-out attributes in photo β a flat facial photo cut to the shape of the face.
- 3. External attributes on top of photo β a flat facial photo with real accessories (glasses, cap, etc.) attached on top.
- 4. Photo mask on actor + external attributes β a full-size photo fixed to an actorβs face; real items such as a hood or wig are added.
- 5. Photo mask on actor, printed attributes β a fixed photo that already contains additional printed attributes.
- 6. Photo mask on actor with eye holes + external attributes β eye openings are cut in the photo; the actor blinks through them while wearing real wig/clothing.
- 7. Photo mask with printed attributes and eye holes β combines printed accessories on the photo with the actorβs live eyes visible through cut-outs.
Potential Use Cases
- Liveness detection R&D: train / benchmark algorithms that separate selfies from 3D mask spoofs with high accuracy.
- iBeta level 2 pre-certification: stress-test PAD models against high-realism 3D mask scenarios before formal audits.
- Cross-material studies: analyse generalisation gaps between silicone, latex, paper and textile attacks for robust deployment.
Keywords: iBeta certification, PAD attacks, Presentation Attack Detection, Antispoofing, Facial Biometrics, Biometric Authentication, Security Systems, Machine Learning Dataset
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25 participants
recorded under signed consent
Dual-device capture:
iOS / Android phones
Diverse representation:
balanced gender mix and broad ethnicity coverage (Caucasian, Black, Asian, Latinx)
5 000 videos
5 000 videos
Active-liveness phases:
fixed, zoom-in, zoom-out
Size of the auto-converted Parquet files:
4.04 kB
Number of rows:
14