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Case Study

AI-Powered Cybersecurity

Scalable Face Recognition Testing, Data Collection & Model Training with Crowdsourced User Insights

Client Context

A global AI-driven cybersecurity firm was conducting large-scale face recognition trials across multiple countries, demographics, environments, and backgrounds. Their goal was to enhance the AI model’s ability to accurately determine user behavior across diverse real-world conditions.

O-Primes Solution: AI-Driven Testing Framework

As India’s leading AI user testing platform, O-Primes designed and deployed a scalable and structured AI testing framework to validate and optimize face recognition algorithms under varied conditions, ensuring robustness and fairness.

Multiscious AI Testing Framework

Automated & Structured AI Testing

  • Defined a structured evaluation matrix to assess the AI application's happy flow, edge cases, and adversarial scenarios.
  • Implemented a Safety Evaluation & Red Teaming approach to detect potential biases, security loopholes, and adversarial vulnerabilities.

Multi-Device & Cross-Platform Compatibility

  • Performed functional testing across diverse device ecosystems to assess the AI model’s adaptability.
  • Ensured compatibility across mobile, desktop, and edge devices under varied configurations and operating environments.

Data Collection at Scale & Model Training

  • Designed an execution matrix covering diverse test cases, including:
  • Indoor vs. outdoor environments
  • Time-based variations: Morning, Afternoon, Night
  • Motion-based analysis: Static vs. Walking
  • Occlusion scenarios: With/without cap, mask, glasses
  • Appearance-based variations: Different hairstyles, dress shades, and facial expressions
  • Collected 20,000+ high-quality training data points across controlled permutations and combinations to ensure extensive model learning.

Multilingual & Cultural Sensitivity Testing

  • Curated a diverse dataset representing users from Asia, South Asia, and MENA regions.
  • Evaluated the model’s performance across different ethnicities, skin tones, and linguistic variations to mitigate biases and enhance fairness.
  • Ensured the AI system’s cultural adaptability through localized testing scenarios.

Testing Execution & Outcome

Expert Testers & Crowdsourced Validation

A team of 350+ expert testers and domain specialists from the O-Primes community executed structured test cases on the AI model.

Rapid Data Scaling

Within 20 working days, over 20,000+ diverse training samples were captured, following strict adherence to predefined testing conditions.

Project Management & Real-time Monitoring

A dedicated project manager orchestrated the testing plan, ensuring real-time tracking, validation, and compliance with the execution framework.

Robust Model Training & AI Optimization

The vast dataset empowered the AI to learn from diverse real-world conditions, enhancing model accuracy, robustness, and fairness.

Conclusion

Leveraging Oprimes’ crowdsourced testing model, the AI-powered cybersecurity solution underwent rigorous safety evaluation, adversarial robustness testing, and cross-demographic validation. By integrating large-scale structured testing, real-world data collection, and AI fairness assessments, the initiative strengthened the model’s security, inclusivity, and performance across global demographics

Build Smarter, Safer AI with Gen AI Testing

Leverage the power of Gen AI & crowdsourced user insights to scale AI performance, security, and compliance.

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CIN : U72900KA2021PTC152684

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