Hussein K. Alzubaidy, Dhiah Al-Shammary, Mohammed Hamzah Abed
In this survey, thirty models for steganography and visual encryption methods have been discussed to provide patients privacy protection.
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NeR-VCP: A Video Content Protection Method Based on Implicit Neural Representation
本文提出NeR-VCP，一种基于隐式神经表示和密钥可控模块的自动视频加密方法，将普通视频加密为隐式神经网络，合法接收方使用预分发模块解密，结合模型压缩减少传输量，在视觉表示、不可感知性和密码学安全性上表现优越。
In this paper, we propose an encryption method for ConvMixer models with a secret key. Encryption methods for DNN models have been studied to achieve adversarial defense, model protection and privacy-preserving image classification. However, the use of conventional encryption methods degrades the performance of models compared with that of plain models. Accordingly, we propose a novel method for encrypting ConvMixer models. The method is carried out on the basis of an embedding architecture that ConvMixer has, and models encrypted with the method can have the same performance as models trained…
Since production-level trained deep neural networks (DNNs) are of a great business value, protecting such DNN models against copyright infringement and unauthorized access is in a rising demand. However, conventional model protection methods focused only the image classification task, and these protection methods were never applied to semantic segmentation although it has an increasing number of applications. In this paper, we propose to protect semantic segmentation models from unauthorized access by utilizing block-wise transformation with a secret key for the first time. Protected models ar…
AMB-FHE: Adaptive Multi-biometric Fusion with Fully Homomorphic Encryption
本文提出AMB-FHE方法，通过全同态加密对多生物特征参考模板进行联合加密，在运行时自适应安全需求，提升了隐私保护与认证灵活性。
Enhancing Privacy in Face Analytics Using Fully Homomorphic Encryption
针对人脸识别嵌入中软生物特征信息易泄露的问题，提出结合全同态加密（FHE）与 PolyProtect 模板保护方案的新技术，通过多项式转换实现不可逆与不可链接的安全模板，在不损害识别准确率的前提下有效防止属性泄露。
Computationally efficient, accurate, and privacy-preserving data storage and retrieval are among the key challenges faced by practical deployments of biometric identification systems worldwide. In this work, a method of protected indexing of biometric data is presented. By utilising feature-level fusion of intelligently paired templates, a multi-stage search structure is created. During retrieval, the list of potential candidate identities is successively pre-filtered, thereby reducing the number of template comparisons necessary for a biometric identification transaction. Protection of the bi…
MedBlindTuner: Towards Privacy-preserving Fine-tuning on Biomedical Images with Transformers and Fully Homomorphic Encryption
本文提出 MedBlindTuner 框架，结合 FHE 与 DEiT，在加密生物医学图像上完成模型训练，并在保持与非加密训练相当准确率的同时保护患者隐私。
In order to prevent leaking input information from intermediate-layer features, this paper proposes a method to revise the traditional neural network into the rotation-equivariant neural network (RENN). Compared to the traditional neural network, the RENN uses d-ary vectors/tensors as features, in which each element is a d-ary number. These d-ary features can be rotated (analogous to the rotation of a d-dimensional vector) with a random angle as the encryption process. Input information is hidden in this target phase of d-ary features for attribute obfuscation. Even if attackers have obtained …
Lightweight True In-Pixel Encryption with FeFET Enabled Pixel Design for Secure Imaging
本文提出一种紧凑的CMOS兼容像素架构SecurePix，通过FeFET的可编程非易失多域极化状态实现真像素加密，有效提升图像传感器端到端安全性。
As billions of personal data being shared through social media and network, the data privacy and security have drawn an increasing attention. Several attempts have been made to alleviate the leakage of identity information from face photos, with the aid of, e.g., image obfuscation techniques. However, most of the present results are either perceptually unsatisfactory or ineffective against face recognition systems. Our goal in this paper is to develop a technique that can encrypt the personal photos such that they can protect users from unauthorized face recognition systems but remain visually…
Smart sensors, devices and systems deployed in smart cities have brought improved physical protections to their citizens. Enhanced crime prevention, and fire and life safety protection are achieved through these technologies that perform motion detection, threat and actors profiling, and real-time alerts. However, an important requirement in these increasingly prevalent deployments is the preservation of privacy and enforcement of protection of personal identifiable information. Thus, strong encryption and anonymization techniques should be applied to the collected data. In this IEEE Big Data …
Semantic Privacy Protection with Utility Preservation for 3D Point Clouds
本文提出基于类别迁移的点云语义加密框架，利用归一化流、LoRA/FiLM和神经常微分方程，在隐藏原始类别的同时保持下游效用，并支持授权恢复。
Attack GAN (AGAN ): A new Security Evaluation Tool for Perceptual Encryption
针对感知加密的隐私保护安全性问题，本文提出基于GAN的AGAN攻击方法，可破解AVIH及传统感知加密方法，可作为评估加密方法鲁棒性的基准工具。
Generative Adversarial Networks for Dental Patient Identity Protection in Orthodontic Educational Imaging
本文提出一种保面积GAN反演技术用于牙科患者图像去身份化，在保护隐私的同时保留关键牙科特征，并通过临床评估验证其对牙科教育和诊断的实用价值。
CFE-PPAR: Compression-friendly encryption for privacy-preserving action recognition leveraging video transformers
本文提出CFE-PPAR，一种压缩友好的加密方法，通过视频变换器实现加密视频的高效识别，优于现有方法。
Security monitoring via ubiquitous cameras and their more extended in intelligent buildings stand to gain from advances in signal processing and machine learning. While these innovative and ground-breaking applications can be considered as a boon, at the same time they raise significant privacy concerns. In fact, recent GDPR (General Data Protection Regulation) legislation has highlighted and become an incentive for privacy-preserving solutions. Typical privacy-preserving video monitoring schemes address these concerns by either anonymizing the sensitive data. However, these approaches suffer …
Privacy-Preserving Object Detection for Vision Transformer-Based Models
针对现有视觉信息保护研究多聚焦图像分类的问题，本文首次提出基于感知加密的ViT模型隐私保护目标检测方法，利用ViT嵌入结构与带密钥的领域适配技术，在ViTdet上验证了其保护视觉信息的同时保持高准确率的效果。
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