Presentation Attack Detection (Liveness / PAD)
Techniques and tests that detect spoofed biometric samples (e.g., masks, replays, synthetics) to ensure the sample is from a live, consenting subject.
Overview
Presentation Attack Detection (PAD) protects biometric systems from spoofs such as printed photos, silicone fingerprints, recorded voices, or AI-generated samples. It’s a cross-cutting layer used with face, voice, fingerprint, iris and other modalities.
How it works
- Capture: Sensor or camera acquires the sample.
- Signal analysis: Algorithms look for cues inconsistent with live traits (e.g., texture, reflectance, micro-motions, audio artifacts).
- Decision & score: The PAD subsystem outputs a score or decision (bona fide vs attack).
- Policy: Systems combine PAD with biometric matching and business rules to accept/deny or request step-up verification.
Common use cases
- Remote onboarding / selfie match
- Contactless border checks
- KYC and high-risk transactions
- Access control and workforce auth
Strengths and limitations
Strengths: Mitigates common spoofs; complements matching; standard metrics for evaluation.
Limitations: Attack diversity; new synthetic media; environment variability; false rejections at strict thresholds.
Key terms
- APCER/BPCER: Core PAD error metrics from ISO/IEC 30107-3.
- PAI (Presentation Attack Instrument): The artifact used to attack.
- Attack potential: Effort/resources required to mount an attack.
References
Vendors using Presentation Attack Detection (Liveness / PAD)
Latest Data Cards
Data Card FaceTec Appoints Cameron D’Ambrosi as Head of Strategic Partnerships for North America and EU
2026-01-06CC-BY-4.0facial-recognitionpadfacetecFaceTec appointed Cameron D’Ambrosi as Head of Strategic Partnerships for North America and the European Union, describing the role as focused on expanding deployments of its 3D face verification and liveness technology.
- FaceTec describes the role as spanning enterprise integrations and public-sector digital identity programs.
- D’Ambrosi is identified as a co-founder of Liminal and host of the State of Identity podcast.
- FaceTec positions liveness detection as a control for AI-enabled fraud and presentation or injection attacks.
Data Card Neurotechnology Reports Level 2 PAD Evaluation for Face Liveness Technology
2026-01-05CC-BY-4.0padfacial-recognitionneurotechnologyNeurotechnology says its face liveness and presentation attack detection technology passed a Level 2 evaluation aligned with ISO/IEC 30107-3, conducted by BixeLab.
- The company describes Level 2 testing as covering more sophisticated presentation attacks than Level 1, including variants beyond printed photos or screen replays.
- Neurotechnology’s announcement does not disclose quantitative metrics or test configuration details.
- ISO/IEC 30107-3 defines testing and reporting requirements for PAD but does not prescribe a universal pass/fail threshold.
Data Card UK Opens Registration for Deepfake Detection Challenge 2026
2025-12-24CC-BY-4.0padUK government bodies opened registration for the Deepfake Detection Challenge 2026, an exercise intended to benchmark and test deepfake detection tools in adversarial scenarios.
- Public materials describe a two-stage format: a benchmarking phase and a scenario-based exercise.
- The initiative is linked to the Home Office, DSIT, The Alan Turing Institute, and the Accelerated Capability Environment.
- The challenge targets risks associated with synthetic media, including identity and security use cases such as remote verification and fraud investigations.