ABSTRACT The Vehicular Social Network (VSN) enables convenient interactions through its unique social characteristics, but simultaneously faces critical security threats—including privacy leakage, malicious information propagation, and social engineering attacks—that endanger system security and compromise user interests. Existing privacy‐preserving schemes exhibit substantial limitations in dynamic VSN environments, as they struggle to simultaneously ensure strong security, real‐time responsiveness, and efficient resource utilization. To overcome these issues, this paper proposes an efficient and reliable message privacy protection scheme leveraging signcryption technology. The scheme restructures the signcryption process into two distinct phases—offline and online—for enhanced efficiency. During the offline phase, the majority of computations are pre‐processed without requiring the actual message content or recipient identity. In the online phase, ciphertexts are assembled rapidly with minimal computational overhead, substantially improving signcryption efficiency and addressing the stringent efficiency and security demands of short‐lived communications in VSN scenarios. Security analysis confirms that the scheme satisfies multiple critical requirements, including message confidentiality, unforgeability, vehicle identity privacy preservation, and traceability of malicious nodes. Performance evaluation indicates that, relative to the latest Certificateless Online/Offline Signcryption (CLOOSC) scheme, the proposed approach achieves a 20% reduction in communication overhead and approximately 50% reduction in signcryption computation costs. This offers a practical and effective solution for secure and efficient message communication in VSN environments.

Automatic colorization of anime line art videos aims to produce color frames given line art frames and reference color images, which is challenging due to various motions and geometric transformations across frame sequences. Existing methods usually utilize the feature maps of reference images directly and treat all the regions in an image equally. However, this may overlook the details of the regions undergoing geometric transformations . To emphasize the regions with significant transformations between the reference and target frames, we propose a Transformation Region Enhancement Network (TRE-Net) to exploit useful reference information and enhance the colorization of key transformation regions with Region Localization Module (RLM) and Feature Enhancement Module (FEM). Specifically, we propose Multi-scale Euclidean Distance Difference (Multi-scale EDD) Maps in RLM which effectively locate geometric transformation regions by contrasting the Euclidean Distance Maps of two line arts and aggregating representations at multiple scales of the network. In addition, FEM is devised to enhance feature learning in the regions with geometric transformation and to ensure proper color alignment. FEM learns locally enhanced features through an attention-gating operation at a low computational cost. With the well-represented key geometric transformation regions, our method exploits the multi-scale reference information well for color alignment, thus produces perceptually pleasing frames. Comprehensive experimental results show that our proposed method is superior to existing methods in terms of the overall quality of colorized anime line art videos.

Automatic sketch colorization is a challenging task in both computer graphics and computer vision since all the color, texture, shading generation have to be created based on the abstract sketch. Besides, it is a subjective task in painting process, which needs illustrators to comprehend drawing priori (DP), such as hue variation, saturation contrast and gray contrast and utilize them in the HSV color space which is closer to human visual cognition system. As such, incorporating supplementary supervision in the HSV color space may be beneficial to sketch colorization. However, previous methods improve the colorization quality only in the RGB color space without considering the HSV color space, often causing results with dull color, inappropriate saturation contrast, and artifacts. To address this issue, we propose a novel sketch colorization method, dual color space guided generative adversarial network (DCSGAN), that considers the complementary information contained in both the RGB and HSV color space. Specifically, we incorporate the HSV color space to construct dual color spaces for supervising our method with a color space transformation (CST) network that learns transformation from the RGB to HSV color space. Then, we propose a DP loss that enables the DCSGAN to generate vivid color images with pixel level supervision. Additionally, a novel dual color space adversarial (DCSA) loss is designed to guide the generator at global level to reduce the artifacts to meet audiences' aesthetic expectations. Extensive experiments and ablation studies demonstrate the superiority of the proposed method over previous state-of-the-art (SOTA) methods.

The key of Weakly Supervised Fine-grained Image Classification (WFGIC) is how to pick out the discriminative regions and learn the discriminative features from them. However, most recent WFGIC methods pick out the discriminative regions independently and utilize their features directly, while neglecting the facts that regions' features are mutually semantic correlated and region groups can be more discriminative. To address these issues, we propose an end-to-end Graph-propagation based Correlation Learning (GCL) model to fully mine and exploit the discriminative potentials of region correlations for WFGIC. Specifically, in discriminative region localization phase, a Criss-cross Graph Propagation (CGP) sub-network is proposed to learn region correlations, which establishes correlation between regions and then enhances each region by weighted aggregating other regions in a criss-cross way. By this means each region's representation encodes the global image-level context and local spatial context simultaneously, thus the network is guided to implicitly discover the more powerful discriminative region groups for WFGIC. In discriminative feature representation phase, the Correlation Feature Strengthening (CFS) sub-network is proposed to explore the internal semantic correlation among discriminative patches' feature vectors, to improve their discriminative power by iteratively enhancing informative elements while suppressing the useless ones. Extensive experiments demonstrate the effectiveness of proposed CGP and CFS sub-networks, and show that the GCL model achieves better performance both in accuracy and efficiency.