Digital Twins (DTs) generate vast sensitive data, demanding secure, dynamic, and fine-grained access control. While Ciphertext-Policy Attribute-Based Encryption (CP-ABE) offers such control, its inherent computational overhead, static nature, and the need to trust outsourcers for complex operations often limit its practical application in evolving DT environments. In this paper, we proposed an Outsourced Attribute-Based Encryption Scheme with Verifiable Policy Update and Dynamic Attributes (OABE-VPUDA) for Digital Twins. OABE-VPUDA empowers Data Owners (DOs) and Data Users (DUs) by enabling verifiable outsourced encryption, where a Public Verifier confirms Cloud Server (CS) computations, and verifiable outsourced de cryption, validated by DUs using an inspired tag mechanism. The scheme further incorporates dynamic attribute management via on-chain expiration tags for timely, fine-grained revocation, and allows DOs to securely and verifiably modify ciphertext access policies with CS assistance. A consortium blockchain underpins these functionalities, significantly enhancing system transparency, auditability, and overall trust in collaborative DT ecosystems. Formal security analysis rigorously demonstrates that the OABE-VPUDA scheme is secure against chosen-plaintext attacks, thereby ensuring robust data confidentiality, and its de sign ensures the verifiability of all critical outsourced operations within a provable security framework. Experimental results from a prototype implementation confirm the scheme's operational efficiency and practical applicability, highlighting its suitability for secure and agile data sharing in resource-aware digital twin applications.

In recent years, sparse voxel-based methods have become the state-of-the-arts for 3D semantic segmentation of indoor scenes, thanks to the powerful 3D CNNs. Nevertheless, being oblivious to the underlying geometry, voxel-based methods suffer from ambiguous features on spatially close objects and struggle with handling complex and irregular geometries due to the lack of geodesic information. In view of this, we present Voxel-Mesh Network (VMNet), a novel 3D deep architecture that operates on the voxel and mesh representations leveraging both the euclidean and geodesic information. Intuitively, the euclidean information extracted from voxels can offer contextual cues representing interactions between nearby objects, while the geodesic information extracted from meshes can help separate objects that are spatially close but have disconnected surfaces. To incorporate such information from the two domains, we design an intra-domain attentive module for effective feature aggregation and an inter-domain attentive module for adaptive feature fusion. Experimental results validate the effectiveness of VMNet: specifically, on the challenging ScanNet dataset for large-scale segmentation of indoor scenes, it outperforms the state-of-the-art SparseConvNet and MinkowskiNet (74.6% versus 72.5% and 73.6% in mIoU) with a simpler network structure (17M versus 30M and 38M parameters).

In unconstrained scenarios, face recognition remains challenging, particularly when faces are occluded. Existing methods generalize poorly due to the distribution distortion induced by unpredictable occlusions. To tackle this problem, we propose a hierarchical segmentation-based mask learning strategy for face recognition, enhancing occlusion-robustness by integrating segmentation representations of occlusion into face recognition in the latent space. We present a novel multi-scale segmentation-based mask learning (MSML) network, which consists of a face recognition branch (FRB), an occlusion segmentation branch (OSB), and hierarchical elaborate feature masking (FM) operators. With the guidance of hierarchical segmentation representations of occlusion learned by the OSB, the FM operators can generate multi-scale latent masks to eliminate mistaken responses introduced by occlusions and purify the contaminated facial features at multiple layers. In this way, the proposed MSML network can effectively identify and remove the occlusions from feature representations at multiple levels and aggregate features from visible facial areas. Experiments on face verification and recognition under synthetic or realistic occlusions demonstrate the effectiveness of our method compared to state-of-the-art methods.

In recent years, sparse voxel-based methods have be-come the state-of-the-arts for 3D semantic segmentation of indoor scenes, thanks to the powerful 3D CNNs. Nevertheless, being oblivious to the underlying geometry, voxel-based methods suffer from ambiguous features on spatially close objects and struggle with handling complex and irregular geometries due to the lack of geodesic information. In view of this, we present Voxel-Mesh Network (VMNet), a novel 3D deep architecture that operates on the voxel and mesh representations leveraging both the Euclidean and geodesic information. Intuitively, the Euclidean information extracted from voxels can offer contextual cues representing interactions between nearby objects, while the geodesic information extracted from meshes can help separate objects that are spatially close but have disconnected surfaces. To incorporate such information from the two domains, we design an intra-domain attentive module for effective feature aggregation and an inter-domain attentive module for adaptive feature fusion. Experimental results validate the effectiveness of VMNet: specifically, on the challenging ScanNet dataset for large-scale segmentation of indoor scenes, it outperforms the state-of-the-art SparseConvNet and MinkowskiNet (74.6% vs 72.5% and 73.6% in mIoU) with a simpler network structure (17M vs 30M and 38M parameters). Code release: https://github.com/hzykent/VMNet

Outliers due to occlusion, pixel corruption, and so on pose serious challenges to face recognition despite the recent progress brought by sparse representation. In this article, we show that robust statistics implemented by the state-of-the-art methods are insufficient for robustness against dense gross errors. By modeling the distribution of coding residuals with a Laplacian-uniform mixture, we obtain a sparse representation that is significantly more robust than the previous methods. The nonconvex error term of the implemented objective function is nondifferentiable at zero and cannot be properly addressed by the usual iteratively reweighted least-squares formulation. We show that an iterative robust coding algorithm can be derived by local linear approximation of the nonconvex error term, which is both effective and efficient. With iteratively reweighted l1minimization of the error term, the proposed algorithm is capable of handling the sparsity assumption of the coding errors more appropriately than the previous methods. Notably, it has the distinct property of addressing error detection and error correction cooperatively in the robust coding process. The proposed method demonstrates significantly improved robustness for face recognition against dense gross errors, either contiguous or discontiguous, as verified by extensive experiments.

Sparse representation based methods have successfully put forward a general framework for robust face recognition through linear reconstruction and sparsity constraints. However, residual modeling in existing works is not yet robust enough when dealing with dense noise. In this paper, we aim at recognizing identities from faces with varying levels of noises of various forms such as occlusion, pixel corruption, or disguise, and take improving the fitting ability of the error model as the key to addressing this problem. To fully capture the characteristics of different noises, we propose a mixed model combining robust sparsity constraint and low-rank constraint, which can deal with random errors and structured errors simultaneously. For random noises such as pixel corruption, we adopt a Laplacian-uniform mixed function for fitting the error distribution. For structured errors like continuous occlusion or disguise, we utilize robust nuclear norm to constrain the rank of the error matrix. An effective iterative reweighted algorithm is then developed to solve the proposed model. Comprehensive experiments were conducted on several benchmark databases for robust face recognition, and the overall results demonstrate that our model is most robust against various kinds of noises, when compared with state-of-the-art methods.

Facial expression recognition (FER) is a challenging problem with important applications. Applying deep learning techniques for dynamic FER is advantageous in terms of automatically generating discriminative expression features. However, inadequate training data in small expression databases aggravates overfitting and hinders the performance of deep networks. Recent studies have shown that dense connectivity in convolutional neural networks can encourage feature sharing and alleviate overfitting when training with small datasets. Still, traditional dense structures are too deep for FER with insufficient training samples. In this paper, we propose a relatively shallow CNN structure with densely connected short paths for FER. Instead of using transition layers to down-sample feature maps between dense blocks, we introduce dense connectivity across pooling to enforce feature sharing in the shallow CNN structure. Extensive experiments show that our method achieves competitive performance on benchmark datasets CK+ and Oulu-CASIA.

Face recognition based on sparse representation has attracted broad interest in recent years. In many existing sparse coding works, the distribution of error term (coding residual) is modeled with a Laplacian or Gaussian function, which leads to an l1-norm or l2-norm minimization problem. However, it is hard to fit the error term satisfactorily in practice, especially when occlusion or corruption exists. In order to improve the robustness of sparse coding algorithms to outliers, we propose an efficient pixel-selection strategy in this paper, which can pick out unspoiled pixels from a seriously damaged face image. However, it is very challenging to determine the positions of the outliers directly. We propose an iterative coding approach to improve the selection. Extensive experiments demonstrate the robustness and effectiveness of the proposed method in the presence of occlusion and corruption.