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.

Macrophotography Images (MPIs) have recently emerged as an active topic due to the development of mobile phone camera technology. A large number of MPIs have been rapidly increasing in many rich visual services, such as smartphones or high-definition monitors. MPIs are often composed of sharp macroimage and blur background, which exhibit different perceptual properties that often lead to different just noticeable difference (JND) estimation. Inspired by this, we formulate the blur concealment (BC) as another factor to determine the total masking effect: the interaction is relatively straightforward with a limited masking effect in the sharp regions, and is complicated with a strong masking effect in the blur parts. Furthermore, texture and orientation adaption and color information weighting are separately incorporated into the contrast masking and color masking. Finally, considering both BC and masking effects, a novel robust watermarking framework based on the proposed blur-guided JND model for MPIs, targeting at further improving the MPIs copyright protection performance. Extensive experiments on MP2020 and Blur Detection data sets show that the applicability of the proposed JND model in the scenario of perceptually MPIs watermarking, and our proposed scheme can outperform the state-of-the-art watermarking schemes by providing better robustness performance at the uniform visual quality.

A novel block-level perceptual image watermarking framework is proposed in this study, including tri-directional correlation and a block-level just noticeable difference (JND) model. Specifically, the difference in the discrete cosine transform (DCT) coefficients of two blocks is calculated based on three directions in the neighborhood, called the tri-directional correlation (TriDC). Additionally, the representative alternating current (AC) coefficients along horizontal, vertical, and diagonal directions, which can describe structural patterns, are projected and merged for TriDC differences. Then, the difference of the DCT coefficient is modulated to a predefined zone depending on the JND-based offset. Finally, the extent of the watermarked AC coefficients is determined with perceptual JND adjustment. The experimental results demonstrate that the proposed scheme can protect most common image processing attacks; and has better robustness compared with recent zone modulation watermarking schemes and traditional watermarking methods.