Because of the high cost of panoramic photographs and the small amount of existing panoramic image datasets, research on the panorama‐based deep learning is restricted. In this paper, we propose a method to generate panoramic images based on a CycleGAN network. First, a selective transfer units module is added to carry out selective transmission and conversion of feature maps. Then, a convolutional block attention module is added to strengthen the image feature processing. Style conversion technology is combined to achieve spring and winter style conversion and night and day style conversion, so as to expand the dataset of panoramic images. The experimental results show that the proposed method is effective in panoramic image generation, and the use of panoramic images for style transfer is also effective in dataset expansion.

In recent years, the plagiarism of comic images has become increasingly prevalent, drawing growing attention to copyright protection within the comic industry. To address the limitations of existing object detection models in capturing the distinctive visual characteristics of comic images, this paper proposes an optimized detection framework, MANGA‐YOLOv8s (MA‐YOLOv8s). Specifically, a large separable kernel attention‐based spatial pyramid pooling (SPPF‐LSKA) module is designed to expand the effective receptive field and enhance multiscale feature aggregation for small‐object detection. The C2f‐DBB module is introduced into the detection head to refine deep feature representation while maintaining lightweight computation. Furthermore, a separated and enhancement attention module (SEAM) is incorporated into the detection heads to improve robustness against scale variation and suppress false detections. Unlike simple combinations of existing modules, these designs form a theoretically motivated and task‐specific integration that adapts the YOLOv8 framework to the structural and stylistic characteristics of comic images. Experiments on the Manga109 dataset demonstrate that MA‐YOLOv8s achieves a 3.7% improvement in mAP and a 3.4% increase in precision compared with YOLOv8s. The proposed method offers both theoretical and practical contributions to the development of efficient detection techniques for comic copyright protection.

Because of the copyright issues of hyperspectral images continue to rise, in this paper, we propose to use a neural network–based watermarking model to protect the copyright. By applying normalization‐based attention module (NAM) to deep dispersed watermarking with synchronization and fusion (DWSF), a NDWSF model is proposed for robust hyperspectral image watermarking. It consists of encoding, decoding, discrimination, and attack modules. The encoding and decoding modules are used for embedding and extracting watermarks. Discrimination module is proposed for improving the quality of watermarked image. The discrimination module and the encoding module are in an adversarial relationship to motivate the encoder to generate watermarks with stronger invisibility. Attack module is employed between embedding and extraction to improve robustness against compression and noise and geometric attacks. In order to more effectively utilize image features for watermarking, a kind of hybrid attention mechanism is employed in embedding and extraction by adding NAM. Experimental results show that the loss convergence and stability in training is improved. The peak signal‐to‐noise ratio of the proposed method is 48.08 dB, higher than other methods about 2.5 dB. The bit error rate of the proposed method is less than 2.5% for various hybrid attacks, showing good robustness.