Towards Reversible De-Identification in Video Sequences Using 3D Avatars and Steganography

TL;DR

This paper introduces a privacy-preserving method for videos that replaces humans with 3D avatars and encodes original images steganographically, maintaining scene naturalness and enabling potential reversibility.

Contribution

It presents a novel de-identification pipeline combining 3D avatar rendering with steganography for reversible privacy protection in videos.

Findings

Qualitative results show effective human concealment.

Steganographic encoding preserves original images within scenes.

Method demonstrates promising feasibility for privacy-preserving video processing.

Abstract

We propose a de-identification pipeline that protects the privacy of humans in video sequences by replacing them with rendered 3D human models, hence concealing their identity while retaining the naturalness of the scene. The original images of humans are steganographically encoded in the carrier image, i.e. the image containing the original scene and the rendered 3D human models. We qualitatively explore the feasibility of our approach, utilizing the Kinect sensor and its libraries to detect and localize human joints. A 3D avatar is rendered into the scene using the obtained joint positions, and the original human image is steganographically encoded in the new scene. Our qualitative evaluation shows reasonably good results that merit further exploration.