In copy-move forgery, the illumination and contrast of tampered and genuine regions are highly consistent, which poses a greater challenge in Copy-Move Forgery Detection (CMFD). In this paper, an end-to-end neural network is proposed based on Adaptive attention and Residual refinement Network (AR-Net). Specifically, position and channel attention features are fused by the adaptive attention mechanism to fully capture context information and enrich the representation of features. Secondly, deep matching is adopted to compute the self-correlation between feature maps, and Atrous Spatial Pyramid Pooling (ASPP) fuses the scaled correlation maps to generate the coarse mask. Finally, the coarse mask is optimized through the residual refinement module, which retains the structure of objects boundary. Extensive experiments, evaluated on CASIAII, COVERAGE and CoMoFoD datasets, demonstrate that the AR-Net has superior performance than state-of-the-art algorithms and can locate tampered and corresponding genuine regions in pixel level. In addition, AR-Net has high robustness on post-processing operations, such as noise, blur, JPEG recompression.

Retrieving an occluded pedestrian remains a challenging problem in person re-identification (re-id). Most existing methods utilize external detectors to disentangle the visible body parts. However, these methods are unstable due to domain bias and consume numerous computing resources. In this paper, we propose a novel and lightweight Part-based Representation Enhancement (PRE) network for occluded re-id that takes full advantages of the local correlations to aggregate distinctive information for local features without relying on auxiliary detectors. First, according to the information qualities of different body parts, we design a reasonable partition strategy to obtain the local features. Next, a Partial Relationship Aggregation (PRA) module is developed to self-mine the visibility of the body and construct a correlation matrix for collecting the information related to pre-defined classes. Following this, we propose an Inter-part Omnibearing Fusion (IOF) module that leverages the occlusion-suppressed class features to enhance the distinctiveness of the local features via feature completion and reverse fusion strategies. During the testing phase, the global and reconstructed local features are concatenated together for re-id without a complex visible region matching algorithm. Extensive experiments on occluded, partial, and holistic re-id benchmarks demonstrate the superiority of PRE over state-of-the-art methods in terms of accuracy and model complexity.

As a spontaneous facial expression, a micro-expression can reveal the psychological responses of human beings. Thus, micro-expression recognition can be widely studied and applied for its potentiality in clinical diagnosis, psychological research, and security. However, micro-expression recognition is a formidable challenge due to the short-lived time frame and low-intensity of the facial actions. In this paper, a sparse spatiotemporal descriptor for micro-expression recognition is developed by using the Enhanced Local Cube Binary Pattern (Enhanced LCBP). The proposed Enhanced LCBP is composed of three complementary binary features containing Spatial Difference Local Cube Binary Patterns (Spatial Difference LCBP), Temporal Direction Local Cube Binary Patterns (Temporal Direction LCBP), and Temporal Gradient Local Cube Binary Patterns (Temporal Gradient LCBP). With the application of Enhanced LCBP, it would no longer be a problem to provide binary features with spatiotemporal domain complementarity to capture subtle facial changes. In addition, due to the redundant information existing among the division grids, which affects the ability of descriptors to distinguish micro-expressions, the Multi-Regional Joint Sparse Learning is designed to perform feature selection for the division grids, thus paying more attention to the critical local regions. Finally, the Multi-kernel Support Vector Machine (SVM) is employed to fuse the selected features for the final classification. The proposed method exhibits great advantage and achieves promising results on four spontaneous micro-expression datasets. Through further observation of parameter evaluation and confusion matrix, the sufficiency and effectiveness of the proposed method are proved.

Most methods address occluded pedestrian Re-Identification (Re-ID) by employing external auxiliary models in the feature output stage of the backbone network to locate visible appearance areas. Nevertheless, these approaches suffer from issues such as occlusion information diffusion and imprecise masks generated by external models, indicating the need for further exploration in the decoupling of pedestrian features from occlusion information. In light of these challenges, we propose an innovative algorithm called Pose-Skeleton guided Cross-attention Representation fusion (PSCR) method. Firstly, we introduce the Visible Appearance Region Attention (VARA) model designed to leverage pose information for guiding the backbone network in effectively distinguishing between occlusion information and pedestrian features at the intermediate layer. By employing a suppression strategy, the model is able to effectively suppress occlusion interference and alleviate the diffusion of occlusion information. Next, to achieve precise localization of pedestrian-specific semantic regions, a groundbreaking Skeletal Area Modeling (SAM) is proposed. Leveraging the principles of mathematical modeling and capitalizing on the efficacy of human keypoint confidence, this module generates finely-grained masks for local skeleton regions and extracts an exhaustive set of local features. Lastly, under the constraints imposed by spatial attention masks, a cross-attention mechanism is employed to fuse the features acquired from the previous two steps with local features. This fusion process results in the generation of enhanced local features that seamlessly integrate aligning high-level semantic information. Extensive experimentation demonstrates that the proposed algorithm exhibits notable performance advancements when compared to existing methodologies.

Image aesthetic evaluation refers to the subjective aesthetic evaluation of images. Computational aesthetics has been widely concerned due to the limitations of subjective evaluation. Aiming at the problem that the existing evaluation methods of image aesthetic quality only extract the low-level features of images and they have a low correlation with human subjective perception, this paper proposes an aesthetic evaluation model based on latent semantic features. The aesthetic features of images are extracted by superpixel segmentation that is based on weighted density POI (Point of Interest), which includes semantic features, texture features, and color features. These features are mapped to feature words by LLC (Locality-constrained Linear Coding) and, furthermore, latent semantic features are extracted using the LDA (Latent Dirichlet Allocation). Finally, the SVM classifier is used to establish the classification prediction model of image aesthetics. The experimental results on the AVA dataset show that the feature coding based on latent semantics proposed in this paper improves the adaptability of the image aesthetic prediction model, and the correlation with human subjective perception reaches 83.75%.

Splicing is the most common operation in image forgery, where the tampered background regions are imported from different images. Illumination maps are inherent attribute of images and provide significant clues when searching for splicing locations. This paper proposes an end-to-end dual-stream network for splicing location, where the illumination stream, which includes Grey-Edge (GE) and Inverse-Intensity Chromaticity (IIC), extract the inconsistent features, and the image stream extracts the global unnatural tampered features. The dual-stream feature in our network is fused through Multiple Feature Pyramid Network (MFPN), which contains richer context information. Finally, a Cluster Region Proposal Network (C-RPN) with spatial attention and an adaptive cluster anchor are proposed to generate potential tampered regions with greater retention of location information. Extensive experiments, which were evaluated on the NIST16 and CASIA standard datasets, show that our proposed algorithm is superior to some state-of-the-art algorithms, because it achieves accurate tampered locations at the pixel level, and has great robustness in post-processing operations, such as noise, blur and JPEG recompression.