The paper introduces a new object detection approach that protects sensitive visual data in test images. It is the first to apply perceptual encryption to object detection, leveraging the embedding structure of Vision Transformers and a key-based domain adaptation to maintain accuracy comparable to unprotected models. Experiments on ViTdet confirm the method’s effectiveness in both accuracy and visual protection.
By Homare Sueyoshi, Kiyoshi Nishikawa, Hitoshi Kiya
The paper argues that evaluating privacy‑enhancing technologies (PETs) solely through image classification is insufficient because classification remains robust to many geometric and local perturbations. It proposes a compute‑aware multi‑task protocol that uses lightweight proxy tasks to assess PETs across various transformations, revealing that PETs with similar classification accuracy can perform very differently on other vision tasks. The study demonstrates the necessity of broader evaluation metrics beyond classification to truly gauge PET effectiveness.
By Leon Ranke, Wolfgang H\"ubner, Ronny Hug, Michael Arens, J\"urgen Beyerer
arXiv:2607. 08867v1 Announce Type: cross Abstract: Cloud-based deep learning enables large-scale medical image analysis but raises significant privacy concerns when sensitive patient images are outsourced for model development.
By Jason Rojas, Jiajie He, Yash Patel, Yuechun Gu, Zeyun Yu, Keke Chen
arXiv:2503.12232v3 Announce Type: replace
Abstract: Aiming to match pedestrian images captured under varying lighting conditions, visible-infrared person re-identification (VI-ReID) has drawn intensi...
By Yan Jiang, Hao Yu, Xu Cheng, Haoyu Chen, Zhaodong Sun, Guoying Zhao
The unprecedented growth of computer vision applications, such as surveillance systems and social media, raises security and visual privacy concerns, especially when data is stored on cloud servers. Image obfuscation offers a way to preserve visual privacy while maintaining an adequate level of usability; thus, it has been a topic of great interest in recent years.
arXiv:2608. 14724v1 Announce Type: cross Abstract: The rapid advancement of intelligent transportation systems and autonomous driving relies heavily on multi-modal urban traffic datasets.
By Mohammed Abdul Al Arafat Tanzin, Rudzidatul Akmam Dziyauddin
arXiv:2409. 01062v4 Announce Type: replace Abstract: Model Inversion (MI) attacks pose a significant privacy threat by reconstructing private training data from machine learning models.
By Viet-Hung Tran, Ngoc-Bao Nguyen, Son T. Mai, Hans Vandierendonck, Ira Assent, Alex Kot, Ngai-Man Cheung
arXiv:2609.13823v1 Announce Type: new
Abstract: Point cloud data face serious semantic privacy risks during acquisition, transmission, and cross-institutional sharing. Existing methods mostly rely on...
By Jinchang zhang, Jiakai Lin, David Crandall, Guoyu Lu
arXiv:2608.28691v1 Announce Type: cross
Abstract: Wearable VLM pipelines promise continuous multimodal assistance from egocentric visual capture: a user asks a task-driven question about the surround...
By Zhimin Li, Pan Wang, Jingxian Chen, Yuantao Tang, Anthony Chen, Qian Lou, Jingtong Hu
arXiv:2608.11645v2 Announce Type: replace
Abstract: Volumetric video streaming turns privacy into a 3D, multi-view problem. Unlike ordinary video, where sensitive content can often be redacted frame...
By Hossein Khalili (UCLA), Philip Do (UCLA), Alexander Vilesov (UCLA), Achuta Kadambi (UCLA), Kittipat Apicharttrisorn (Nokia Bell Labs), Nader Sehatbakhsh (UCLA)
arXiv:2608.23012v1 Announce Type: new
Abstract: Image matching is a core component of applications such as Simultaneous Localization and Mapping (SLAM), Visual Localization, and Structure from Motion...
By Francesco Vultaggio, Predrag Djindjic, Markus Gerke, Sebastian Tschiatschek, Phillipp Fanta-Jende
The paper investigates the security of a selective coordinate encryption scheme for point clouds used in volumetric video. By training PointNet and Random Forest models on partially encrypted data, the authors show that fully encrypting all X coordinates is difficult to reconstruct, whereas encrypting every second X coordinate allows accurate recovery of the hidden values. The study highlights that the encryption granularity critically determines the vulnerability of the scheme.
By Mohammad Waquas Usmani, Susmit Shannigrahi, Michael Zink