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SimaClassify or Incode for Liveness + Deepfake? 4-Point Decision Guide

Security teams evaluating SimaClassify alongside Incode shape how organizations fight the new wave of deep-fake and spoof attacks. This guide breaks the choice into four evidence-backed pillars.

Why the SimaClassify vs Incode Decision Matters in 2026

The landscape of identity fraud has shifted dramatically. Identity fraud losses in the United States alone could reach US$40 billion annually by 2027, according to Incode research, while we've witnessed a 30x deepfake increase from 2022 to 2023. These aren't just abstract threats; they're actively targeting authentication systems across industries.

Liveness detection is a technique used to determine whether a biometric sample - usually a face - is being presented by a real, live human at the time of capture. This technology has become the frontline defense against sophisticated spoofing attempts that range from simple printed photos to AI-generated deepfakes. As fraud techniques evolve, the ability to accurately verify genuine human presence in real-time has transformed from optional security enhancement to critical infrastructure.

1. Detection Coverage: Digital, Physical & Emerging Attack Types

The breadth of attack surface coverage often determines whether a liveness solution holds up under real-world conditions. Modern threat actors deploy a wide spectrum of techniques, and vendors differ significantly in their defensive capabilities.

Research from the UniAttackData+ challenge reveals the scale of modern attack sophistication: For each participant, 54 types of attacks were simulated, comprising 14 physical and 40 digital attack variants, resulting in a total of 679,097 high-quality forged videos. This massive dataset underscores why comprehensive coverage matters; attackers aren't limiting themselves to one methodology.

The LivDet-Face 2024 competition introduced groundbreaking attack vectors that many systems still struggle to detect: Three novel PAIs were introduced: projection on 2D, projection on 3D, and bobblehead models of bona fide subjects. These presentation attack instruments represent the cutting edge of physical spoofing techniques that go beyond traditional masks or photos.

Incode addresses this expanding threat landscape through a multi-layered approach. According to their published specifications, Our liveness detection technology combines digital, physical, and evasion spoof detection, doubling the effectiveness and detection rate over using these methods independently. This integrated strategy recognizes that modern attacks often combine multiple vectors simultaneously.

For organizations evaluating these solutions alongside broader AI infrastructure needs, the lessons from SimaBit's AI processing demonstrate how specialized AI can deliver measurable improvements when properly integrated into existing workflows.

2. Passive Liveness: Balancing UX With Fraud Resilience

The shift toward passive liveness detection represents a fundamental change in how organizations approach user verification. Unlike active methods that require specific actions, Passive liveness detection happens invisibly in the background. The system captures a user's face as usual, and then runs analysis in real time to determine if the face is genuine.

Speed becomes critical when implementing passive systems at scale. Performance benchmarks show significant variation across vendors, with Keyless' passive liveness engine performs checks in under 300 milliseconds - five times faster than many competitors. This sub-second latency enables seamless user experiences while maintaining security integrity.

Certification standards provide crucial validation for passive liveness implementations. Incode's solution stands out with World-class security standards: As the first iBeta certified passive liveness solution, our technology is fully compliant with ISO 30107 standards and NIST guidelines, ensuring robust protection. These certifications aren't merely checkboxes; they represent rigorous third-party validation of attack resistance.

Model Benchmarks & Latency

Recent research on CNN architectures reveals significant performance variations across different model implementations. Testing across standard datasets shows DenseNet201 achieving the highest performance (98.5% on NUAA, 97.71% on Replay-Attack), demonstrating the importance of architecture selection in achieving high accuracy rates.

For deployment scenarios prioritizing efficiency, MobileNetV2 proved the most efficient model for real-time applications (latency: 15 ms, memory usage: 45 MB, energy consumption: 30 mJ). This represents a crucial trade-off consideration; organizations must balance detection accuracy against computational resources and response times.

Incode's real-world performance validation provides concrete evidence of effectiveness. Their technology underwent independent testing where In 2024 the Georgia Department of Driver's Services (GA DDS) performed an independent testing of Incode's Liveness according to iBeta level 2 protocols, and we achieved 0 APCER (false positive rate / false acceptance of fraudulent users) and 0 BPCER (false negative rate / false rejections of genuine users). Zero false positives and negatives in government testing represents a significant achievement in passive detection.

3. Deployment Flexibility: SaaS, On-Prem or On-Device

Deployment architecture decisions fundamentally impact data sovereignty, implementation speed, and ongoing control. Organizations face distinct trade-offs between cloud convenience and on-premises security.

For organizations prioritizing rapid deployment, SaaS models offer compelling advantages: Choose SaaS when you want: Faster start as you don't need to procure or allocate hardware within your company and set up a new instance. Cloud deployments eliminate infrastructure overhead while providing immediate scalability.

However, data residency requirements often mandate on-premises solutions. Facia's implementation demonstrates this approach: Facia's on-premise face recognition, liveness detection, and deepfake detection allow you to have complete control over your data, and ensure there is no possibility of leaks or interception. This complete data control becomes essential for regulated industries handling sensitive biometric information.

DeepTempo's documentation reveals the technical complexity of on-premises deployments: Complete guide for deploying Tempo on-premises with Docker, Kubernetes, and GPU support. Includes prerequisites, deployment steps, and troubleshooting. Organizations must factor in not just initial deployment but ongoing maintenance and scaling considerations.

The choice mirrors broader infrastructure decisions, as seen in the GenAI whitepaper, which emphasizes how deployment flexibility enables organizations to adapt to evolving security requirements while maintaining performance standards.

4. Total Cost of Ownership & Future-Proofing

Total cost of ownership extends far beyond initial licensing fees. Organizations must evaluate per-transaction pricing, infrastructure requirements, and long-term market dynamics.

The broader context of API-based services provides crucial perspective: Per-call pricing can start to eat into your margins once usage scales. This same dynamic applies to liveness detection services, where transaction volumes can grow exponentially.

Face recognition pricing models vary dramatically across the market: Face recognition can cost anywhere from $0.001 to $0.05 per use, or $20 to $10,000+ monthly, depending on scale and features. These ranges underscore the importance of accurate volume forecasting when evaluating solutions.

Market growth projections signal increasing investment in this space. According to industry analysis, Identity fraud losses in the United States alone could reach US$40 billion annually by 2027, driving demand for robust detection capabilities. Simultaneously, The global face biometric liveness detection market will grow to surpass US$250 million dollars by 2027, indicating rapid market expansion.

Transaction volumes are set to explode: face liveness detection transactions are forecasted to exceed 50 Billion Annually by 2027 – more than doubling the projected totals for 2025. Organizations implementing solutions today must ensure their chosen platform can scale cost-effectively with this growth.

Regulated sectors are leading adoption, with Businesses in heavily regulated sectors are leading a surge in adoption of liveness detection to protect against spoof attacks on face biometric systems. This regulatory pressure adds another dimension to TCO calculations; compliance failures can result in penalties far exceeding technology costs.

Choosing the Right Path Forward

The decision between evaluating SimaClassify alongside Incode ultimately depends on weighing four critical factors against your organization's specific requirements. Detection breadth, passive liveness performance, deployment flexibility, and total cost of ownership each present distinct trade-offs that must align with your risk tolerance and growth trajectory.

As SimaBit's preprocessing approach minimizes implementation risk by allowing organizations to test and deploy the technology incrementally while maintaining their existing encoding infrastructure, the same principle applies to liveness detection: start with pilot deployments, validate performance against your specific threat landscape, and scale based on proven results.

Sima Labs understands the complexity of these decisions, having developed solutions that balance cutting-edge AI capabilities with practical deployment considerations. Whether optimizing video processing with SimaBit or implementing next-generation security measures, the key lies in choosing partners who can deliver both immediate value and long-term scalability.

Should we pick SimaClassify or Incode for liveness & deepfake verification?

Both vendors deliver certified passive liveness, but the choice hinges on four pillars: 1) attack-surface coverage across digital and physical spoofs; 2) passive-liveness latency and user experience; 3) deployment fit (SaaS vs. on-prem for data sovereignty); 4) three-year total cost of ownership, including per-call fees and compliance overhead. Weight each pillar against your risk appetite and growth plans; headline accuracy alone is never enough.

What is passive liveness detection?

Passive liveness runs silently during a selfie capture, analysing micro-texture, light reflection and depth cues to confirm that the face is live without requiring head turns or eye-blinks. Because checks finish in under 300 ms, they offer a friction-free experience while meeting ISO 30107-3 PAD standards used by regulators worldwide.

Frequently Asked Questions

What are the four pillars in this decision guide?

The guide focuses on detection coverage, passive liveness performance and user experience, deployment flexibility (SaaS, on-prem, on-device), and total cost of ownership. Teams should weigh each pillar against risk tolerance, compliance needs, and growth plans rather than relying on headline accuracy alone.

What defines comprehensive detection coverage for liveness and deepfake defense?

Comprehensive coverage addresses both digital and physical spoofs, including emerging vectors. Recent benchmarks span 54 attack types across 679k forged videos and introduce novel PAIs like projection-based and bobblehead attacks, underscoring the need for multi-layered defenses.

How fast should passive liveness be, and which certifications matter?

Sub-second latency preserves UX; leading passive engines process in roughly a few hundred milliseconds. Certifications aligned to ISO 30107 and iBeta level protocols, alongside independent evaluations such as GA DDS reporting 0 APCER and 0 BPCER for a vendor in 2024, signal robust real-world resilience.

Which deployment model should we choose: SaaS, on-prem, or on-device?

Use SaaS for rapid time-to-value and elastic scale when data residency is not a blocker. Choose on-prem or on-device when data sovereignty, offline operation, or strict latency controls dominate; plan for Kubernetes, GPU, and DevOps overhead in these models and pilot before broad rollout.

How do we estimate total cost of ownership (TCO) for liveness and deepfake verification?

Go beyond license fees to model per-call pricing, infrastructure, compliance, and support. Unit costs can range from fractions of a cent to several cents per use, and with transactions forecast in the tens of billions annually, small per-call deltas compound materially at scale.

How do Sima Labs resources inform this evaluation?

Sima Labs highlights deployment flexibility and integration-first workflows in its GenAI whitepaper at https://www.simalabs.ai/gen-ad. Our press update on the SimaBit integration with Dolby Hybrik at https://www.simalabs.ai/pr shows how we deliver production-grade performance without disrupting existing pipelines—principles applicable to security stack decisions.

Sources

SimaClassify or Incode for Liveness + Deepfake? 4-Point Decision Guide

Security teams evaluating SimaClassify alongside Incode shape how organizations fight the new wave of deep-fake and spoof attacks. This guide breaks the choice into four evidence-backed pillars.

Why the SimaClassify vs Incode Decision Matters in 2026

The landscape of identity fraud has shifted dramatically. Identity fraud losses in the United States alone could reach US$40 billion annually by 2027, according to Incode research, while we've witnessed a 30x deepfake increase from 2022 to 2023. These aren't just abstract threats; they're actively targeting authentication systems across industries.

Liveness detection is a technique used to determine whether a biometric sample - usually a face - is being presented by a real, live human at the time of capture. This technology has become the frontline defense against sophisticated spoofing attempts that range from simple printed photos to AI-generated deepfakes. As fraud techniques evolve, the ability to accurately verify genuine human presence in real-time has transformed from optional security enhancement to critical infrastructure.

1. Detection Coverage: Digital, Physical & Emerging Attack Types

The breadth of attack surface coverage often determines whether a liveness solution holds up under real-world conditions. Modern threat actors deploy a wide spectrum of techniques, and vendors differ significantly in their defensive capabilities.

Research from the UniAttackData+ challenge reveals the scale of modern attack sophistication: For each participant, 54 types of attacks were simulated, comprising 14 physical and 40 digital attack variants, resulting in a total of 679,097 high-quality forged videos. This massive dataset underscores why comprehensive coverage matters; attackers aren't limiting themselves to one methodology.

The LivDet-Face 2024 competition introduced groundbreaking attack vectors that many systems still struggle to detect: Three novel PAIs were introduced: projection on 2D, projection on 3D, and bobblehead models of bona fide subjects. These presentation attack instruments represent the cutting edge of physical spoofing techniques that go beyond traditional masks or photos.

Incode addresses this expanding threat landscape through a multi-layered approach. According to their published specifications, Our liveness detection technology combines digital, physical, and evasion spoof detection, doubling the effectiveness and detection rate over using these methods independently. This integrated strategy recognizes that modern attacks often combine multiple vectors simultaneously.

For organizations evaluating these solutions alongside broader AI infrastructure needs, the lessons from SimaBit's AI processing demonstrate how specialized AI can deliver measurable improvements when properly integrated into existing workflows.

2. Passive Liveness: Balancing UX With Fraud Resilience

The shift toward passive liveness detection represents a fundamental change in how organizations approach user verification. Unlike active methods that require specific actions, Passive liveness detection happens invisibly in the background. The system captures a user's face as usual, and then runs analysis in real time to determine if the face is genuine.

Speed becomes critical when implementing passive systems at scale. Performance benchmarks show significant variation across vendors, with Keyless' passive liveness engine performs checks in under 300 milliseconds - five times faster than many competitors. This sub-second latency enables seamless user experiences while maintaining security integrity.

Certification standards provide crucial validation for passive liveness implementations. Incode's solution stands out with World-class security standards: As the first iBeta certified passive liveness solution, our technology is fully compliant with ISO 30107 standards and NIST guidelines, ensuring robust protection. These certifications aren't merely checkboxes; they represent rigorous third-party validation of attack resistance.

Model Benchmarks & Latency

Recent research on CNN architectures reveals significant performance variations across different model implementations. Testing across standard datasets shows DenseNet201 achieving the highest performance (98.5% on NUAA, 97.71% on Replay-Attack), demonstrating the importance of architecture selection in achieving high accuracy rates.

For deployment scenarios prioritizing efficiency, MobileNetV2 proved the most efficient model for real-time applications (latency: 15 ms, memory usage: 45 MB, energy consumption: 30 mJ). This represents a crucial trade-off consideration; organizations must balance detection accuracy against computational resources and response times.

Incode's real-world performance validation provides concrete evidence of effectiveness. Their technology underwent independent testing where In 2024 the Georgia Department of Driver's Services (GA DDS) performed an independent testing of Incode's Liveness according to iBeta level 2 protocols, and we achieved 0 APCER (false positive rate / false acceptance of fraudulent users) and 0 BPCER (false negative rate / false rejections of genuine users). Zero false positives and negatives in government testing represents a significant achievement in passive detection.

3. Deployment Flexibility: SaaS, On-Prem or On-Device

Deployment architecture decisions fundamentally impact data sovereignty, implementation speed, and ongoing control. Organizations face distinct trade-offs between cloud convenience and on-premises security.

For organizations prioritizing rapid deployment, SaaS models offer compelling advantages: Choose SaaS when you want: Faster start as you don't need to procure or allocate hardware within your company and set up a new instance. Cloud deployments eliminate infrastructure overhead while providing immediate scalability.

However, data residency requirements often mandate on-premises solutions. Facia's implementation demonstrates this approach: Facia's on-premise face recognition, liveness detection, and deepfake detection allow you to have complete control over your data, and ensure there is no possibility of leaks or interception. This complete data control becomes essential for regulated industries handling sensitive biometric information.

DeepTempo's documentation reveals the technical complexity of on-premises deployments: Complete guide for deploying Tempo on-premises with Docker, Kubernetes, and GPU support. Includes prerequisites, deployment steps, and troubleshooting. Organizations must factor in not just initial deployment but ongoing maintenance and scaling considerations.

The choice mirrors broader infrastructure decisions, as seen in the GenAI whitepaper, which emphasizes how deployment flexibility enables organizations to adapt to evolving security requirements while maintaining performance standards.

4. Total Cost of Ownership & Future-Proofing

Total cost of ownership extends far beyond initial licensing fees. Organizations must evaluate per-transaction pricing, infrastructure requirements, and long-term market dynamics.

The broader context of API-based services provides crucial perspective: Per-call pricing can start to eat into your margins once usage scales. This same dynamic applies to liveness detection services, where transaction volumes can grow exponentially.

Face recognition pricing models vary dramatically across the market: Face recognition can cost anywhere from $0.001 to $0.05 per use, or $20 to $10,000+ monthly, depending on scale and features. These ranges underscore the importance of accurate volume forecasting when evaluating solutions.

Market growth projections signal increasing investment in this space. According to industry analysis, Identity fraud losses in the United States alone could reach US$40 billion annually by 2027, driving demand for robust detection capabilities. Simultaneously, The global face biometric liveness detection market will grow to surpass US$250 million dollars by 2027, indicating rapid market expansion.

Transaction volumes are set to explode: face liveness detection transactions are forecasted to exceed 50 Billion Annually by 2027 – more than doubling the projected totals for 2025. Organizations implementing solutions today must ensure their chosen platform can scale cost-effectively with this growth.

Regulated sectors are leading adoption, with Businesses in heavily regulated sectors are leading a surge in adoption of liveness detection to protect against spoof attacks on face biometric systems. This regulatory pressure adds another dimension to TCO calculations; compliance failures can result in penalties far exceeding technology costs.

Choosing the Right Path Forward

The decision between evaluating SimaClassify alongside Incode ultimately depends on weighing four critical factors against your organization's specific requirements. Detection breadth, passive liveness performance, deployment flexibility, and total cost of ownership each present distinct trade-offs that must align with your risk tolerance and growth trajectory.

As SimaBit's preprocessing approach minimizes implementation risk by allowing organizations to test and deploy the technology incrementally while maintaining their existing encoding infrastructure, the same principle applies to liveness detection: start with pilot deployments, validate performance against your specific threat landscape, and scale based on proven results.

Sima Labs understands the complexity of these decisions, having developed solutions that balance cutting-edge AI capabilities with practical deployment considerations. Whether optimizing video processing with SimaBit or implementing next-generation security measures, the key lies in choosing partners who can deliver both immediate value and long-term scalability.

Should we pick SimaClassify or Incode for liveness & deepfake verification?

Both vendors deliver certified passive liveness, but the choice hinges on four pillars: 1) attack-surface coverage across digital and physical spoofs; 2) passive-liveness latency and user experience; 3) deployment fit (SaaS vs. on-prem for data sovereignty); 4) three-year total cost of ownership, including per-call fees and compliance overhead. Weight each pillar against your risk appetite and growth plans; headline accuracy alone is never enough.

What is passive liveness detection?

Passive liveness runs silently during a selfie capture, analysing micro-texture, light reflection and depth cues to confirm that the face is live without requiring head turns or eye-blinks. Because checks finish in under 300 ms, they offer a friction-free experience while meeting ISO 30107-3 PAD standards used by regulators worldwide.

Frequently Asked Questions

What are the four pillars in this decision guide?

The guide focuses on detection coverage, passive liveness performance and user experience, deployment flexibility (SaaS, on-prem, on-device), and total cost of ownership. Teams should weigh each pillar against risk tolerance, compliance needs, and growth plans rather than relying on headline accuracy alone.

What defines comprehensive detection coverage for liveness and deepfake defense?

Comprehensive coverage addresses both digital and physical spoofs, including emerging vectors. Recent benchmarks span 54 attack types across 679k forged videos and introduce novel PAIs like projection-based and bobblehead attacks, underscoring the need for multi-layered defenses.

How fast should passive liveness be, and which certifications matter?

Sub-second latency preserves UX; leading passive engines process in roughly a few hundred milliseconds. Certifications aligned to ISO 30107 and iBeta level protocols, alongside independent evaluations such as GA DDS reporting 0 APCER and 0 BPCER for a vendor in 2024, signal robust real-world resilience.

Which deployment model should we choose: SaaS, on-prem, or on-device?

Use SaaS for rapid time-to-value and elastic scale when data residency is not a blocker. Choose on-prem or on-device when data sovereignty, offline operation, or strict latency controls dominate; plan for Kubernetes, GPU, and DevOps overhead in these models and pilot before broad rollout.

How do we estimate total cost of ownership (TCO) for liveness and deepfake verification?

Go beyond license fees to model per-call pricing, infrastructure, compliance, and support. Unit costs can range from fractions of a cent to several cents per use, and with transactions forecast in the tens of billions annually, small per-call deltas compound materially at scale.

How do Sima Labs resources inform this evaluation?

Sima Labs highlights deployment flexibility and integration-first workflows in its GenAI whitepaper at https://www.simalabs.ai/gen-ad. Our press update on the SimaBit integration with Dolby Hybrik at https://www.simalabs.ai/pr shows how we deliver production-grade performance without disrupting existing pipelines—principles applicable to security stack decisions.

Sources

SimaClassify or Incode for Liveness + Deepfake? 4-Point Decision Guide

Security teams evaluating SimaClassify alongside Incode shape how organizations fight the new wave of deep-fake and spoof attacks. This guide breaks the choice into four evidence-backed pillars.

Why the SimaClassify vs Incode Decision Matters in 2026

The landscape of identity fraud has shifted dramatically. Identity fraud losses in the United States alone could reach US$40 billion annually by 2027, according to Incode research, while we've witnessed a 30x deepfake increase from 2022 to 2023. These aren't just abstract threats; they're actively targeting authentication systems across industries.

Liveness detection is a technique used to determine whether a biometric sample - usually a face - is being presented by a real, live human at the time of capture. This technology has become the frontline defense against sophisticated spoofing attempts that range from simple printed photos to AI-generated deepfakes. As fraud techniques evolve, the ability to accurately verify genuine human presence in real-time has transformed from optional security enhancement to critical infrastructure.

1. Detection Coverage: Digital, Physical & Emerging Attack Types

The breadth of attack surface coverage often determines whether a liveness solution holds up under real-world conditions. Modern threat actors deploy a wide spectrum of techniques, and vendors differ significantly in their defensive capabilities.

Research from the UniAttackData+ challenge reveals the scale of modern attack sophistication: For each participant, 54 types of attacks were simulated, comprising 14 physical and 40 digital attack variants, resulting in a total of 679,097 high-quality forged videos. This massive dataset underscores why comprehensive coverage matters; attackers aren't limiting themselves to one methodology.

The LivDet-Face 2024 competition introduced groundbreaking attack vectors that many systems still struggle to detect: Three novel PAIs were introduced: projection on 2D, projection on 3D, and bobblehead models of bona fide subjects. These presentation attack instruments represent the cutting edge of physical spoofing techniques that go beyond traditional masks or photos.

Incode addresses this expanding threat landscape through a multi-layered approach. According to their published specifications, Our liveness detection technology combines digital, physical, and evasion spoof detection, doubling the effectiveness and detection rate over using these methods independently. This integrated strategy recognizes that modern attacks often combine multiple vectors simultaneously.

For organizations evaluating these solutions alongside broader AI infrastructure needs, the lessons from SimaBit's AI processing demonstrate how specialized AI can deliver measurable improvements when properly integrated into existing workflows.

2. Passive Liveness: Balancing UX With Fraud Resilience

The shift toward passive liveness detection represents a fundamental change in how organizations approach user verification. Unlike active methods that require specific actions, Passive liveness detection happens invisibly in the background. The system captures a user's face as usual, and then runs analysis in real time to determine if the face is genuine.

Speed becomes critical when implementing passive systems at scale. Performance benchmarks show significant variation across vendors, with Keyless' passive liveness engine performs checks in under 300 milliseconds - five times faster than many competitors. This sub-second latency enables seamless user experiences while maintaining security integrity.

Certification standards provide crucial validation for passive liveness implementations. Incode's solution stands out with World-class security standards: As the first iBeta certified passive liveness solution, our technology is fully compliant with ISO 30107 standards and NIST guidelines, ensuring robust protection. These certifications aren't merely checkboxes; they represent rigorous third-party validation of attack resistance.

Model Benchmarks & Latency

Recent research on CNN architectures reveals significant performance variations across different model implementations. Testing across standard datasets shows DenseNet201 achieving the highest performance (98.5% on NUAA, 97.71% on Replay-Attack), demonstrating the importance of architecture selection in achieving high accuracy rates.

For deployment scenarios prioritizing efficiency, MobileNetV2 proved the most efficient model for real-time applications (latency: 15 ms, memory usage: 45 MB, energy consumption: 30 mJ). This represents a crucial trade-off consideration; organizations must balance detection accuracy against computational resources and response times.

Incode's real-world performance validation provides concrete evidence of effectiveness. Their technology underwent independent testing where In 2024 the Georgia Department of Driver's Services (GA DDS) performed an independent testing of Incode's Liveness according to iBeta level 2 protocols, and we achieved 0 APCER (false positive rate / false acceptance of fraudulent users) and 0 BPCER (false negative rate / false rejections of genuine users). Zero false positives and negatives in government testing represents a significant achievement in passive detection.

3. Deployment Flexibility: SaaS, On-Prem or On-Device

Deployment architecture decisions fundamentally impact data sovereignty, implementation speed, and ongoing control. Organizations face distinct trade-offs between cloud convenience and on-premises security.

For organizations prioritizing rapid deployment, SaaS models offer compelling advantages: Choose SaaS when you want: Faster start as you don't need to procure or allocate hardware within your company and set up a new instance. Cloud deployments eliminate infrastructure overhead while providing immediate scalability.

However, data residency requirements often mandate on-premises solutions. Facia's implementation demonstrates this approach: Facia's on-premise face recognition, liveness detection, and deepfake detection allow you to have complete control over your data, and ensure there is no possibility of leaks or interception. This complete data control becomes essential for regulated industries handling sensitive biometric information.

DeepTempo's documentation reveals the technical complexity of on-premises deployments: Complete guide for deploying Tempo on-premises with Docker, Kubernetes, and GPU support. Includes prerequisites, deployment steps, and troubleshooting. Organizations must factor in not just initial deployment but ongoing maintenance and scaling considerations.

The choice mirrors broader infrastructure decisions, as seen in the GenAI whitepaper, which emphasizes how deployment flexibility enables organizations to adapt to evolving security requirements while maintaining performance standards.

4. Total Cost of Ownership & Future-Proofing

Total cost of ownership extends far beyond initial licensing fees. Organizations must evaluate per-transaction pricing, infrastructure requirements, and long-term market dynamics.

The broader context of API-based services provides crucial perspective: Per-call pricing can start to eat into your margins once usage scales. This same dynamic applies to liveness detection services, where transaction volumes can grow exponentially.

Face recognition pricing models vary dramatically across the market: Face recognition can cost anywhere from $0.001 to $0.05 per use, or $20 to $10,000+ monthly, depending on scale and features. These ranges underscore the importance of accurate volume forecasting when evaluating solutions.

Market growth projections signal increasing investment in this space. According to industry analysis, Identity fraud losses in the United States alone could reach US$40 billion annually by 2027, driving demand for robust detection capabilities. Simultaneously, The global face biometric liveness detection market will grow to surpass US$250 million dollars by 2027, indicating rapid market expansion.

Transaction volumes are set to explode: face liveness detection transactions are forecasted to exceed 50 Billion Annually by 2027 – more than doubling the projected totals for 2025. Organizations implementing solutions today must ensure their chosen platform can scale cost-effectively with this growth.

Regulated sectors are leading adoption, with Businesses in heavily regulated sectors are leading a surge in adoption of liveness detection to protect against spoof attacks on face biometric systems. This regulatory pressure adds another dimension to TCO calculations; compliance failures can result in penalties far exceeding technology costs.

Choosing the Right Path Forward

The decision between evaluating SimaClassify alongside Incode ultimately depends on weighing four critical factors against your organization's specific requirements. Detection breadth, passive liveness performance, deployment flexibility, and total cost of ownership each present distinct trade-offs that must align with your risk tolerance and growth trajectory.

As SimaBit's preprocessing approach minimizes implementation risk by allowing organizations to test and deploy the technology incrementally while maintaining their existing encoding infrastructure, the same principle applies to liveness detection: start with pilot deployments, validate performance against your specific threat landscape, and scale based on proven results.

Sima Labs understands the complexity of these decisions, having developed solutions that balance cutting-edge AI capabilities with practical deployment considerations. Whether optimizing video processing with SimaBit or implementing next-generation security measures, the key lies in choosing partners who can deliver both immediate value and long-term scalability.

Should we pick SimaClassify or Incode for liveness & deepfake verification?

Both vendors deliver certified passive liveness, but the choice hinges on four pillars: 1) attack-surface coverage across digital and physical spoofs; 2) passive-liveness latency and user experience; 3) deployment fit (SaaS vs. on-prem for data sovereignty); 4) three-year total cost of ownership, including per-call fees and compliance overhead. Weight each pillar against your risk appetite and growth plans; headline accuracy alone is never enough.

What is passive liveness detection?

Passive liveness runs silently during a selfie capture, analysing micro-texture, light reflection and depth cues to confirm that the face is live without requiring head turns or eye-blinks. Because checks finish in under 300 ms, they offer a friction-free experience while meeting ISO 30107-3 PAD standards used by regulators worldwide.

Frequently Asked Questions

What are the four pillars in this decision guide?

The guide focuses on detection coverage, passive liveness performance and user experience, deployment flexibility (SaaS, on-prem, on-device), and total cost of ownership. Teams should weigh each pillar against risk tolerance, compliance needs, and growth plans rather than relying on headline accuracy alone.

What defines comprehensive detection coverage for liveness and deepfake defense?

Comprehensive coverage addresses both digital and physical spoofs, including emerging vectors. Recent benchmarks span 54 attack types across 679k forged videos and introduce novel PAIs like projection-based and bobblehead attacks, underscoring the need for multi-layered defenses.

How fast should passive liveness be, and which certifications matter?

Sub-second latency preserves UX; leading passive engines process in roughly a few hundred milliseconds. Certifications aligned to ISO 30107 and iBeta level protocols, alongside independent evaluations such as GA DDS reporting 0 APCER and 0 BPCER for a vendor in 2024, signal robust real-world resilience.

Which deployment model should we choose: SaaS, on-prem, or on-device?

Use SaaS for rapid time-to-value and elastic scale when data residency is not a blocker. Choose on-prem or on-device when data sovereignty, offline operation, or strict latency controls dominate; plan for Kubernetes, GPU, and DevOps overhead in these models and pilot before broad rollout.

How do we estimate total cost of ownership (TCO) for liveness and deepfake verification?

Go beyond license fees to model per-call pricing, infrastructure, compliance, and support. Unit costs can range from fractions of a cent to several cents per use, and with transactions forecast in the tens of billions annually, small per-call deltas compound materially at scale.

How do Sima Labs resources inform this evaluation?

Sima Labs highlights deployment flexibility and integration-first workflows in its GenAI whitepaper at https://www.simalabs.ai/gen-ad. Our press update on the SimaBit integration with Dolby Hybrik at https://www.simalabs.ai/pr shows how we deliver production-grade performance without disrupting existing pipelines—principles applicable to security stack decisions.