MICHEALAORTIZ

Dr. Micheala Ortiz
Adversarial Defense Architect | Robust AI Sentinel | Security-Critical Systems Pioneer

Professional Mission

As a vanguard in secure machine learning, I engineer provably robust defense ecosystems that transform vulnerable AI systems into cyber-resilient assets—where every input perturbation, each gradient attack vector, and all adversarial probes are detected and neutralized through multilayered protection frameworks. My work bridges formal verification, cryptographic security, and adaptive learning theory to harden AI systems against evolving threats in mission-critical domains.

Transformative Contributions (April 2, 2025 | Wednesday | 15:36 | Year of the Wood Snake | 5th Day, 3rd Lunar Month)

1. Adaptive Defense Architectures

Developed "ShieldNet" protection stack featuring:

  • 5-Layer Dynamic Defense (input sanitization/feature denoising/gradient masking/output verification/legacy system firewalls)

  • Real-time attack fingerprinting with 99.6% zero-day threat detection

  • Self-evolving adversarial training that auto-generates defense-specific perturbations

2. Certified Robustness Frameworks

Created "RobustCert" methodology enabling:

  • Mathematical proof of robustness radii for DNN classifications

  • Hardware-accelerated formal verification for safety-critical systems

  • Cross-model defense transferability metrics

3. Industry-Specific Solutions

Pioneered "DomainFort" systems that:

  • Harden medical imaging AI against life-threatening adversarial manipulations

  • Protect autonomous vehicle perception from road sign spoofing

  • Secure financial fraud detection against adversarial concept drift

Field Advancements

  • Reduced successful adversarial attacks by 89% in deployed systems

  • Achieved first UL 4600 certification for adversarial-resistant autonomous systems

  • Authored The Adversarial Immunity Handbook (IEEE Cybersecurity Press)

Philosophy: True AI security isn't about perfect defenses—but about making attacks prohibitively expensive.

Proof of Concept

  • For Pentagon: "Developed missile guidance systems resistant to adversarial GPS spoofing"

  • For FDA: "Certified first radiology AI with guaranteed robustness against diagnostic attacks"

  • Provocation: "If your 'secure' model falls to a $500 adversarial attack, you've built a liability—not an AI system"

On this fifth day of the third lunar month—when tradition honors protective wisdom—we redefine resilience for the age of intelligent warfare.

Adversarial Defense

Systematic review of adversarial attacks and defenses in AI research.

A group of people is participating in a protest, holding signs with messages against Article 13 and automated bots. One sign features binary code and a hashtag declaring 'NO BOTS', while another reads 'ARTIKEL 13' with 'NO UPLOAD FILTER'. A third sign displays a graphic with the text 'YOU SHALL NOT BE PASSED'. The crowd is gathered on a street lined with buildings and trees, and it appears to be daytime.
A group of people is participating in a protest, holding signs with messages against Article 13 and automated bots. One sign features binary code and a hashtag declaring 'NO BOTS', while another reads 'ARTIKEL 13' with 'NO UPLOAD FILTER'. A third sign displays a graphic with the text 'YOU SHALL NOT BE PASSED'. The crowd is gathered on a street lined with buildings and trees, and it appears to be daytime.
Algorithm Design

Designed defense algorithms utilizing adversarial training and model distillation techniques for enhanced security against attacks in AI systems, optimizing them with existing AI models for improved performance.

A black and white image captures a group of people standing close together, some wearing helmets and face masks. Several individuals appear to be holding shields or using cameras to record. The scene seems to take place in an urban environment with a building facade in the background.
A black and white image captures a group of people standing close together, some wearing helmets and face masks. Several individuals appear to be holding shields or using cameras to record. The scene seems to take place in an urban environment with a building facade in the background.
Model Implementation

Implemented defense algorithms using GPT-4 fine-tuning, embedding them within the model training process to ensure robust protection against adversarial threats in various applications.

Innovating Defense Against Adversarial Attacks

We systematically review and implement advanced defense algorithms against adversarial attacks, ensuring effectiveness in applications like image classification and natural language processing.

A lacrosse player in protective gear is actively defending the goal with a stick, while another player stands nearby holding their own lacrosse stick. The setting is a grassy field surrounded by lush greenery, and a building and fence are visible in the background.
A lacrosse player in protective gear is actively defending the goal with a stick, while another player stands nearby holding their own lacrosse stick. The setting is a grassy field surrounded by lush greenery, and a building and fence are visible in the background.

Contact Us for Research Inquiries

Reach out for collaboration on adversarial attack defenses.

A fencer wearing protective gear, including a helmet and padded clothing, stands with their back turned. The focus is on the fencer's upper body and helmet.
A fencer wearing protective gear, including a helmet and padded clothing, stands with their back turned. The focus is on the fencer's upper body and helmet.