A Privacy-Preserving Content-Based Image Retrieval Scheme Allowing Mixed Use Of Encrypted And Plain Images

Known-plaintext attack and ciphertext-only attack for encrypted single-pixel imaging

In many previous works, a single-pixel imaging (SPI) system is constructed as an optical image encryption system. Unauthorized users are not able to reconstruct the plaintext image from the ciphertext intensity sequence without knowing the illumination pattern key. However, little cryptanalysis about encrypted SPI has been investigated in the past. In this work, we propose a known-plaintext attack scheme and a ciphertext-only attack scheme to an encrypted SPI system for the first time. The known-plaintext attack is implemented by interchanging the roles of illumination patterns and object imag…

A Privacy-Preserving Image Retrieval Scheme with a Mixture of Plain and EtC Images

In this paper, we propose a novel content-based image-retrieval scheme that allows us to use a mixture of plain images and compressible encrypted ones called "encryption-then-compression (EtC) images." In the proposed scheme, extended SIMPLE descriptors are extracted from EtC images as well as from plain ones, so the mixed use of plain and encrypted images is available for image retrieval. In an experiment, the proposed scheme was demonstrated to have almost the same retrieval performance as that for plain images, even with a mixture of plain and encrypted images.

An Image Identification Scheme of Encrypted JPEG Images for Privacy-Preserving Photo Sharing Services

We propose an image identification scheme for double-compressed encrypted JPEG images that aims to identify encrypted JPEG images that are generated from an original JPEG image. To store images without any visual sensitive information on photo sharing services, encrypted JPEG images are generated by using a block-scrambling-based encryption method that has been proposed for Encryption-then-Compression systems with JPEG compression. In addition, feature vectors robust against JPEG compression are extracted from encrypted JPEG images. The use of the image encryption and feature vectors allows us…

Privacy-Preserving Deep Neural Networks with Pixel-based Image Encryption Considering Data Augmentation in the Encrypted Domain

We present a novel privacy-preserving scheme for deep neural networks (DNNs) that enables us not to only apply images without visual information to DNNs for both training and testing but to also consider data augmentation in the encrypted domain for the first time. In this paper, a novel pixel-based image encryption method is first proposed for privacy-preserving DNNs. In addition, a novel adaptation network is considered that reduces the influence of image encryption. In an experiment, the proposed method is applied to a well-known network, ResNet-18, for image classification. The experimenta…

Robust Privacy-Preserving Motion Detection and Object Tracking in Encrypted Streaming Video

Video privacy leakage is becoming an increasingly severe public problem, especially in cloud-based video surveillance systems. It leads to the new need for secure cloud-based video applications, where the video is encrypted for privacy protection. Despite some methods that have been proposed for encrypted video moving object detection and tracking, none has robust performance against complex and dynamic scenes. In this paper, we propose an efficient and robust privacy-preserving motion detection and multiple object tracking scheme for encrypted surveillance video bitstreams. By analyzing the p…

面向加密医学影像的隐私保护AI：安全诊断与学习框架

Privacy-Preserving AI for Encrypted Medical Imaging: A Framework for Secure Diagnosis and Learning

针对医疗AI应用中的患者隐私问题，提出结合AES-CBC加密、JPEG2000压缩与Masked-CNN的加密医学影像隐私保护诊断框架，在公开数据集上验证其性能接近未加密模型，兼具实用性与可扩展性。