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The aim is to present an offline system, PixelMe, for high-accuracy facial and object anonymization.

January 18, 2026Open Access

Use Cases and Challenges of Ethical Offline Anonymization with YOLOv8: PixelMe on CPU-Only Systems

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The aim is to present an offline system, PixelMe, for high-accuracy facial and object anonymization.
Developed a privacy-preserving system based on the YOLOv8 architecture.
Integrated FFmpeg with a CPU-optimized detection pipeline for performance enhancement.
Evaluated the system on low-resolution and high-resolution video datasets under CPU-only conditions.
Achieved reliable multi-class detection for facial and object recognition.
Successfully anonymized visuals in near-real-time during evaluations.
Enhanced usability for non-experts through a graphical interface.

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Offline system demonstrates effective facial and object anonymization in various video resolutions, suggesting practical privacy solutions.

Mohamed Boucetta-Alberto Medina (2026) studied this question.

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Offline system demonstrates effective facial and object anonymization in various video resolutions, suggesting practical privacy solutions.