Orientation-Aware Reversible Data Hiding With Brainstorming Optimization for UAV Aerial Images

Abstract

In recent years, with the rapid development of unmanned aerial vehicle (UAV), aerial images have extended across various industries such as intelligent building, agriculture, transportation, and Industry 4.0. Notably, the security of UAV-assisted data acquisition during transmission has become a critical concern. The reversible data hiding (RDH) method can hide data in aerial images for transmission and ensure secure communication. In general, an aerial image may exhibit substantially different orientation regularity from a natural scene image. This casts major challenges to the RDH method, for which existing approaches lack effective mechanisms to capture such content type variations, and thus are difficult to generalize from one type to another. In this paper, the orientation-aware selectivity mechanism is introduced to achieve an accurate orientation-aware prediction along different directions in local regions with different structure regularity. Furthermore, we propose a progressive brainstorming optimization algorithm (BSO)-guided optimal PSNR value strategy, which can obtain a superior perceptual performance and the corresponding thresholds by further exploring the pixel correlations within the UAV aerial images. Experimental results on the USC-SIPI Miscellaneous dataset and two challenging aerial datasets, including the USC-SIPI High Altitude Aerial Imagery dataset and the Kaggle dataset, demonstrate that the proposed framework enhances the imperceptibility powerfully in marked UAV aerial images and ensures sufficient embedding capacity effectively. The average PSNR of the marked image obtained by the proposed method is 63.85 dB when embedded with 30,000 bits of data, which is an improvement of 0.59 dB compared to the current state-of-the-art RDH methods.