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Functionality

Stealcut's AI model applies invisible adversarial noise to user images, disrupting deepfake face-generation models while preserving the original visual quality. The added noise remains virtually imperceptible to the human eye, yet it prevents deepfake models from reliably detecting or extracting the face from a protected image, significantly reducing their ability to generate realistic outputs.

Use Potential

If applied to a platform, the AI model developed by Stealcut could help protect user-hosted images by making them more resistant to deepfake misuse. This may enable companies to reduce fraud risk, prevent identity spoofing, and protect users from unauthorized AI-generated content. The technology can operate without requiring user intervention and can be integrated in a way that does not disrupt the platform's existing UX. Ultimately, it can enhance user trust and support compliance with emerging AI safety and security standards.

Technical Workflow

Stealcut's AI model is being refined to achieve real-time processing speeds and cost-efficient operation, with the goal of making it available as both an API and an SDK once these capabilities are fully met. In the future, the API is intended to operate as a managed cloud endpoint, while the SDK is planned for on-device or on-premise deployment in sensitive environments. Integration is being designed to require only a simple call within existing image-processing pipelines.

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About Us

StealCut safeguards trust and safety in society through technology.

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Timeline

Started foundational research on adversarial AI protection for facial images.

2024.08

Completed core POC validation with a team of Seoul National University alumni.

2025.03

Filed core domestic patent (10-2025-0120429).

2025.08

Established a research advisory partnership with Korea University.

2025.09

Executing the Ministry-backed Deep-Tech Pre-Startup Package program.

2025.12

Filed an additional patent (10-2025-0206218).

2026.01

Won 2nd place in the 2025 Yonsei University Pre-Startup Package Deep-Tech Tech-IR competition.