Modern deep face recognition systems are crucial for identity verification and authentication in high-stakes sectors such as security, finance, and law enforcement. However, their reliability is increasingly threatened by the rapid advancement of deepfake technology. Despite considerable attention, comprehensive evaluation of how emerging deepfake generation methods affect reliability of face recognition systems remains lacking. In this paper, we introduce a principled taxonomy and a benchmarking framework that structure deepfake attacks by intent and generative mechanism, enabling consistent and comprehensive assessment. Numerous results show that diverse deepfake attacks commonly exceed 70% success and, in some regimes, surpass 90%. Built on this foundation, we investigate the underlying mechanisms across different deepfake techniques, providing not only broad empirical evaluation but also valuable insight into what drives attack success. Our in-depth analysis reveals that success is not governed by visual quality, but rather by the degree of identity controllability.

Face recognition is widely adopted in our daily life in recent years. It usually relies on sophisticated techniques to achieve high accuracy in identifying or verifying the identities of given face images. Artificial intelligence (AI), especially deep learning, is a popular technique used in face recognition due to its high accuracy, known as the deep face recognition. However, the reliability of the deep face recognition models becomes a concern, especially in security-critical applications. The main challenge is the "black-box" nature of the sophisticated internal structure of the models. Explainable AI has emerged as a solution that provides meaningful explanations to help humans understand the complicated internal structure of the deep learning models, and increase the transparency and interpretability of the "black box". However, this is often at the cost of model accuracy. In this paper, we propose an approach to increasing both the accuracy and interpretation of quantitatively explainable AI models for face recognition. It increases the accuracy of the explainable face recognition models by applying improved loss functions and enhances quantitative interpretability by adding a new visualisation feature. The proposed approach is validated using advanced deep face recognition models and is compared with existing approaches to demonstrate its better performance.

The timely and accurate land cover mapping with remote sensing images played a huge role in ecosystem monitoring. However, due to the spectral variability and spatial complexity of high-resolution remote sensing images, it is often difficult to find an efficient method to achieve accurate land cover classification. To explore useful contextual and hierarchical features in remote sensing images, this paper proposes a U-shaped object graph neural network (U-OGNN), which is mainly composed of self-adaptive graph construction (SAGC), hierarchical graph encoder, and decoder. For self-adaptive graph construction, the similarity measurement is applied to generate contextual-aware graph structure, by feeding deep features extracted from convolution and multi-layer attention operations. Graph encoder and decoder fuse multi-level information over different scales by capturing hierarchical features of adjacent objects. In this way, the proposed method is able to predict land-cover types by considering multi-level contextual information accurately. Experiments on GID land-cover classification datasets, the overall accuracies of the U-OGNN reach 87.81 %.

Land cover is a commonly used index for characterizing land surfaces and the corresponding human or natural processes. Remote sensing-based land cover maps of different times can be compared to extract changes in land cover. However, remote sensing images reflect only the instantaneous state of the Earth's surface. Spectral characteristics cannot correctly reflect the actual state due to the differences caused by seasonal changes, and this inability results in the limited classification accuracy of land cover products. Spurious changes will occur when products of different time phases are compared. A method of spurious change detection based on a geo-eco zoning rule base is proposed in this study to solve this problem. The structure and content of the rule base are designed, and a rule base management system is established to identify spurious changes. Land cover products of Southeast Asia are selected as the experimental area to verify the accuracy of the change patches after eliminating spurious changes. Results show that the accuracy of change detection is improved by using the rule base of geo-eco zoning to identify spurious changes.

A method based on cosegmentation is applied to change detection to segment image patches belonging to each image. The image patches have the characteristics of spatial correspondence in multi-temporal images and precise boundary in its own image. By construction and optimization of energy function that consists of change feature item and image feature item, both of spectrum and shape change can successfully be given the description between different temporal images. The optimal segment results of multi-temporal images are achieved as the energy function reaches its minimum, in this process, the method of min-cut/max-flow will be used.

Urban built-up area information is pivotal to understand complex drivers and mechanisms in global climate change application. However, built-up area extraction using Landsat time series data is a challenging task due to spatial-temporal expression and modeling of land cover types. To provide insights into the intra-annual dynamics of land use change, focusing on how time series characteristics improves recognition of urban , this paper presents an alternative method to built-up area extraction using intra-annual time series of Landsat images. The central premise of the approach is that time series characteristics is firstly expressed by using spectral data, index and feature. The random forests algorithm is then used in classification process for built-up area extraction. The proposed method is further compared with methods using single temporal Landsat data, using features selected by laplacian score and using different classifiers. Results demonstrate that the proposed method improves the accuracy of urban area extraction.

This paper attempts to: (a) examine the defect spectrum between lead free and tin lead PCBA in a volume production environment; and (b) identify any test and inspection needs, such as the AXI, in relation to lead free testing. This information assists the PCBA manufacturer when transitioning to a lead free environment.