With the rapid development of smart medical care, copyright security for medical images is becoming increasingly important. To improve medical images storage and transmission safety, this paper proposes a robust zero-watermarking algorithm for medical images by fusing Dual-Tree Complex Wavelet Transform (DTCWT), Hessenberg decomposition, and Multi-level Discrete Cosine Transform (MDCT). First, the low-frequency sub-band of the medical image is obtained through the DTCWT and MDCT. Then Hessenberg decomposition is used to construct the visual feature vector. Meanwhile, the encryption of the watermarking image by combining cryptographic algorithms, third-party concepts, and chaotic sequences enhances the algorithm’s security. In the proposed algorithm, zero-watermarking technology is utilized to assure the medical images’ completeness. Compared with the existing algorithms, the proposed algorithm has good robustness and invisibility and can efficiently extract the watermarking image and resist different attacks.

With the development of unmanned aerial vehicle (UAV) remote sensing technology, target detection based on UAV images has increasingly become a hot spot for research. Aiming at the problems of many small target instances, complex backgrounds and difficult feature extraction in UAV images, we propose a UAV aerial image target detection algorithm called BLUR-YOLO. First, the h-swish activation function is used in the backbone network and the neck network to increase the expressiveness of the model. Second, an attention mechanism (CoordAttention) is added to the bottleneck layer of the backbone network, thereby increasing the weight of valid information and suppressing background noise interference. Finally, by removing redundant nodes of the path aggregation network (PANet), adding additional connections, and using BlurPool instead of the downsampling method, a feature pyramid network (Blur-PANet) is proposed to effectively fuse multilayer features. Experimental results on the VisDrone public dataset show that the proposed drone image object detection model (BLUR-YOLO) is 1.2% better than the YOLOv4 algorithm, which proves the effectiveness of the method.