Selective Encryption of the Versatile Video Coding Standard

TL;DR

This paper presents a novel method for selective encryption of the VVC video coding standard, ensuring format compliance and constant bitrate, significantly reducing video quality while maintaining robustness against attacks.

Contribution

A new algorithm for format-compliant, constant bitrate selective encryption of VVC syntax elements, integrated into the reference software, with demonstrated effectiveness and efficiency.

Findings

Encryption drastically reduces video quality.

Encryption is robust against various attack types.

Bitstream size increase is between 15% and 26%.

Abstract

Versatile video coding (VVC) is the next generation video coding standard developed by the joint video experts team (JVET) and released in July 2020. VVC introduces several new coding tools providing a significant coding gain over the high efficiency video coding (HEVC) standard. It is well known that increasing the coding efficiency adds more dependencies in the video bitstream making format-compliant encryption with the standard more challenging. In this paper we tackle the problem of selective encryption of the VVC standard in format-compliant and constant bitrate. These two constraints ensure that the encrypted bitstream can be decoded by any VVC decoder while the bitrate remains unchanged by the encryption. The selective encryption of all possible VVC syntax elements is investigated. A new algorithm is proposed to encrypt in format-compliant and constant bitrate the transform coefficients (TCs) together with other syntax elements at the level of the entropy encoder. The proposed solution was integrated and assessed under the VVC reference software model version 6.0. Experimental results showed that the encryption drastically decreases the video quality while the encryption is robust against several types of attacks. The encryption space is estimated in the range of 15% to 26% of the bitstream size resulting in a lightweight encryption process. The web page of this work is available at https://gugautie.github.io/sevvc/.