Independent analysis that transforms complex image data into clear, court-ready evidence.
Quandary Peak Research analyzes AI-driven computer vision systems and traditional image processing technologies, delivering clear, defensible findings on face recognition, object detection, image classification, and video analytics for litigation, regulatory reviews and technical due diligence.
Attorneys, corporate counsel, and regulators often rely on an image processing and computer vision expert witness to explain how algorithms actually function, what data they were trained on, and whether they meet claimed performance standards.
Expert witnesses assess system integrity, accuracy, and reliability—producing objective, reproducible analyses and court-ready explanations that stand up under scrutiny.
Quandary Peak served as an expert witness in a case involving alleged privacy violation and demographic bias in a facial recognition system used for identity verification. Our team analyzed the dataset, reviewed facial recognition algorithmics and decision thresholds, and replicated test scenarios to evaluate privacy concerns, detection accuracy, and demographic fairness.
The resulting report delivered clear, data-backed findings that explained how algorithmic training respected privacy considerations and how those design choices influenced real-world outcomes—helping counsel present a fact-based, technically sound narrative supported by expert testimony.
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We provide independent, technically rigorous expert analysis and consulting for image and video technologies used in litigation, due diligence, and regulatory contexts.
Assess training data, model architecture, preprocessing, and performance metrics for accuracy, bias, and reproducibility in AI computer vision systems.
Assess face recognition algorithms for false match rates, demographic performance, and compliance with privacy standards.
Analyze object detection, pattern recognition, and scene recognition systems for technical soundness and evidentiary reliability.
Test motion tracking, event detection, and optical flow algorithms to confirm real-time system performance.
Support IP litigation through claim charting, code review, and Evidence of Use (EoU) assessments.
Replicate and document performance results using established benchmarks in deep learning for vision and AI testing frameworks.
Image processing and computer vision disputes often hinge on subtle technical details—how a model was trained, which thresholds were used, or whether test conditions matched real-world deployment. Quandary Peak helps clients avoid these pitfalls through transparent, reproducible evaluations. Common risks include:
Biometric data, surveillance footage, and user images can create privacy risks when consent, retention, or access controls are inadequate.
Performance measured under controlled conditions may not reflect how an algorithm performs with real-world data and environments.
Unrepresentative training data or model design choices can produce inconsistent outcomes across demographic groups.
Limited visibility into model logic and decision-making can make system behavior difficult to explain, test, or defend.
Missing model records, test results, or development history can hinder technical validation and complicate IP analysis.
Failure to address applicable privacy, biometric data, or AI requirements can increase legal and regulatory exposure.
Understanding how image processing and computer vision systems operate is essential for attorneys, compliance professionals, and executives evaluating these technologies. Concepts such as object detection, facial recognition, optical flow, and algorithmic auditing can shape how a system’s capabilities and limitations are understood.
In litigation, the technical questions often lead to more consequential ones. How was a model trained and tested? Are its results reliable under real-world conditions? What data does it collect or process? Can its conclusions be independently evaluated and reproduced?
These questions can affect patent and trade secret disputes, privacy claims, allegations of algorithmic bias, and challenges to technical evidence. Our experts examine the underlying technology, distinguish supported conclusions from assumptions, and explain complex findings in clear, defensible terms.
Award-winning computer scientist and software engineer Dr. Iman Sadeghi specializes in delivering expert reports, declarations, and testimony as well as source code analysis for software litigation. He has been published in prestigious peer-reviewed scientific journals and conferences and brings a wealth of experience from his tenure at tech giants such as Google, Walt Disney Animation Studios, and ILM.
Gautham conducts rigorous technical analysis to support expert reports and testimony in complex software-related litigation. With 10+ years of experience in digital forensics, ML, and IP strategy, he brings expertise in synthetic media detection, AI robustness, and reverse engineering to deliver actionable insights.
Jason has over 30 years of experience in software engineering and litigation consulting. He has worked on more than 148 lawsuits and testified in 36+ cases across the U.S. and Canada, with expertise in software patents, copyright infringement, trade secret misappropriation, and large privacy class actions.