UAV image encryption protects privacy while keeping useful scene details
A Category-Selective Encryption Framework for Privacy Protection of UAV Images
Cryptography and SecurityComputer Vision and Pattern Recognition
Summary
Drones capture images that often include sensitive things like people or cars, raising privacy concerns. The authors propose a system that first identifies what objects are in the image, then selectively encrypts only the sensitive categories the user wants to protect. This approach keeps the rest of the image clear, allowing further analysis without losing useful information. Two encryption methods are offered: one that pixelates sensitive parts but can restore details later, and another that fully encrypts data but can be perfectly reversed.
What this means in practice
- •For drone surveillance teams: Encrypt only sensitive objects in drone images to protect privacy while allowing analysis of other scene elements.
- •For security camera integrators: Implement selective encryption of specific object categories to balance privacy and usability in aerial surveillance footage.
Authors
Ting Yan, Minghui Shang, Haolang Sun, Xu Wang, Yamin Han, Mingze He
Abstract
Imagery captured by unmanned aerial vehicle (UAV) systems increasingly raises privacy concerns, as UAV aerial surveillance routinely captures sensitive objects such as pedestrians and vehicles alongside ordinary scene content. Existing privacy-preserving approaches typically employ class-agnostic saliency detection to indiscriminately obfuscate all visually salient regions, leading to unnecessary information loss and hindering downstream scene analysis. This paper proposes a category-selective encryption framework for privacy-preserving UAV surveillance. The core advance over prior class-agnostic approaches is semantic-level selectivity: the system first recognizes what is present in the scene via an 80-class detector, then encrypts only user-specified sensitive categories while leaving non-sensitive objects and background intact for downstream analytics. The framework supports two complementary encryption schemes: Thumbnail-Preserving Encryption (TPE), which applies adaptive pixelation with a learnable blend parameter and recovers content through a jointly trained Enhanced U-Net, and XOR stream cipher encryption, which offers parameter-free, mathematically exact reversibility. The architectural design, composite loss formulation, and joint training methodology are presented in detail, establishing a foundation for practical privacy-preserving UAV surveillance that balances protection strength and scene utility.