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/.
Evaluating Selective Encryption Against Gradient Inversion Attacks
Information Networking Institute（信息网络研究所）；Carnegie Mellon University（卡内基梅隆大学）；TensorOpera AI；Department of Electrical and Computer Engineering（电气与计算机工程系）
Pune Institute of Computer Technology（浦那计算机技术学院）；Indian Institute of Technology Madras（印度理工学院马德拉斯分校）
随着移动设备和相机设备的普及，图像形式的数字内容急剧增加。由于个人生活不断被记录在图片中，因此将其丢失给窃听者的风险是一个令人严重关切的问题。辅存是用于存储个人和其他图像的最优选介质。我们的工作涉及这些图像的安全性。虽然加密是确保图像安全的最佳方法，但完全加密和解密是一个计算密集型过程。此外，随着相机变得越来越好，图像质量，因此，像素密度大大增加。增加的像素密度使得加密和解密更昂贵。因此，我们深入研究了基于感兴趣区域的选择性加密和选择性模糊。我们只对图像的选定区域进行编码，而不是加密或模糊整张照片。我们提出了一个比较分析的部分和完全加密的照片。这种编码将帮助我们降低加密开销，而不会影响安全性。由于解密时间的减少，利用这种技术的应用程序将变得更加可用。此外，模糊图像比加密图像更具可读性，使我们能够定义安全级别。我们利用Mask-RCNN（基于区域的卷积神经网络）和YOLO（You Only Look Once）等机器学习算法来选择感兴趣的区域。这些算法为物体识别设定了新的基准。我们开发了一个端到端的系统来展示我们的选择性加密的想法。
TT-SEAL: TTD-Aware Selective Encryption for Adversarially-Robust and Low-Latency Edge AI
Department of Intelligent Semiconductor Engineering, Chung-Ang University, Seoul, Korea（智能半导体工程系， Chung-Ang 大学）；AI Edge SoC Research Section, Electronics and Telecommunications Research Institute, Daejeon, Korea（AI 边缘 SoC 研究部门，电子电信研究所）
SecureDrive-FL: Joint Differential Privacy and Gradient-Aware Selective Homomorphic Encryption for Federated Driver Monitoring
Institute of Industrial Automation and Software Engineering, University of Stuttgart（斯图加特大学工业自动化与软件工程研究所）；Eindhoven University of Technology (TU/e)（埃因霍温理工大学）
A Selective Homomorphic Encryption Approach for Faster Privacy-Preserving Federated Learning
A. Korkmaz Dept. of Electrical Engineering \& Computer Science, The University of Missouri, Columbia, USA P. Rao Dept. of Electrical Engineering \& Computer Science, The University of Missouri, Columbia, USA
