Face recognition is widely used for biometric authentication in applications such as mobile devices, financial services, intelligent surveillance, access control, and border security. However, face recognition systems remain vulnerable to presentation attacks, including printed photographs, replay attacks, and three-dimensional masks. Although recent deep learning-based face anti-spoofing (FAS) methods have achieved substantial improvements, many existing approaches still involve considerable computational cost, provide limited emphasis on fine-grained spoof artifacts, and face challenges in effectively integrating heterogeneous representations. To address these limitations, this paper proposes  
a Lightweight Face Anti-Spoofing Framework with Synthetic Multi-Modal Representation, Spoof Artifact Enhancement, and Adaptive Feature Fusion. The framework starts from a single RGB facial image and constructs complementary Depth and Near-Infrared (NIR) representations using a Depth and Near-Infrared Construction Module (DNCM), rather than requiring dedicated Depth or NIR sensors. A shared EfficientNetV2 backbone is then employed to extract features from the three representations with reduced computational redundancy. The proposed Spoof Artifact Enhancement Module (SAEM) emphasizes subtle spoof-specific visual cues, while Cross-Modal Consistency Learning (CMCL) reduces representation discrepancies across the constructed modalities. Subsequently, the Adaptive Feature Fusion Module (AFFM) dynamically weights the refined representations according to their discriminative contribution. Extensive experiments on CelebA-Spoof, CASIA-SURF, HQ-WMCA, and SiW-M demonstrate the effectiveness of the proposed framework. The framework achieves accuracies of 98.16%, 97.54%, 99.08%, and 97.18%, with corresponding ACER values of 2.87%, 4.36%, 2.03%, and 4.18%, respectively. Across five independent runs, statistical analysis further indicates consistent performance with significant improvements over the selected baseline. In addition, the framework requires only 9.8 million parameters and 2.1 GFLOPs and achieves an inference speed of 82 FPS, demonstrating a favourable balance between detection effectiveness and computational efficiency. The results indicate that synthetic multi-modal representation combined with explicit spoof artifact enhancement and adaptive feature fusion can provide an efficient solution for robust and real-time face anti-spoofing.

Face recognition systems are increasingly deployed in security-critical applications, but remain vulnerable to presentation attacks such as printed photographs, replay videos, and three-dimensional masks. Although recent face anti-spoofing methods exploit complementary information from RGB, depth, and near-infrared (NIR) representations, existing approaches primarily focus on feature fusion and do not explicitly model the intrinsic consistency relationships among 
these representations. This limitation can reduce robustness against sophisticated spoofing attacks and cross-dataset variations.
This paper proposes a novel Cross-Modal Consistency Learning (CMCL) framework for robust face anti-spoofing using the original RGB input together with RGB-derived depth and NIR representations. The depth and NIR representations are constructed from RGB inputs during the modality preprocessing stage and subsequently processed together with RGB using independent Swin Transformer encoders. A cross-modal attention fusion module adaptively integrates complementary appearance, geometric, and spectral information, while a consistency learning module explicitly encourages feature coherence for genuine samples and emphasizes cross-modal discrepancies associated with spoof attacks. The consistency module is used only during training and removed during inference, avoiding additional deployment overhead.
Extensive experiments are conducted on CASIA-SURF, WMCA, CelebA-Spoof, and MSU-MFSD using intra-dataset, cross-dataset, ablation, feature-space, modality-consistency, and statistical analyses. The proposed CMCL achieves an ACER of 0.70% and accuracy of 99.3% on CASIA-SURF, while achieving an ACER of 2.55% and accuracy of 97.9% on WMCA. The results demonstrate improved spoof detection performance, representation discrimination, and cross-dataset generalization compared with the evaluated state-of-the-art methods. The proposed framework provides a consistency-driven and computationally practical approach for robust face anti-spoofing.

Unmanned Aerial Vehicles (UAVs) have become an effective solution for establishing emergency communication in post-disaster environments where conventional infrastructure is damaged. However, limited UAV battery capacity and unstable connectivity significantly reduce communication reliability and operational coverage. To address these challenges, this paper proposes an energy-efficient UAV-assisted communication framework based on Weighted Global Search Matrix Level (WGSML) clustering and optimal trajectory optimization for device-to-device (D2D) communication. The proposed WGSML method performs energy-aware cluster formation and cluster-head selection using residual energy, signal-to-noise ratio, and neighbourhood density. A Hidden Markov Model (HMM) is employed for routing optimization, while Q-learning-based resource allocation is utilized to determine optimal UAV trajectories and maximize residual energy utilization. Simulation results demonstrate that the proposed approach improves energy harvesting performance, reduces outage probability, minimizes computational runtime, and enhances spectral efficiency compared with existing clustering methods. The proposed framework provides reliable and sustainable communication support for post-disaster emergency response scenarios.

Image segmentation is one of the most important steps in computer vision and image processing. Image segmentation is dividing the image into meaningful regions based on similarity pixels. We propose a new segmentation algorithm based on de-noising of images, good segmentation results depends on the noisy free images. This means that, we may not get the proper segmentation results in the presence of noise. For this, image pre-processing stage is necessary to denoise the image. An image segmentation result depends on the pre-processing results. In this paper, proposed a new integrating approach based on de-noising and segmentation which is called Level Set Segmentation of Images using Block Matching Local SVD Operator Based Sparsity and TV Regularization (BMLSVD-TV). The proposed method is dividing into two stages, in the first stage images are de-noised based on BMLSVDTV algorithm. De-noising images is a crucial aspect of image processing, there are a few factors to keep in mind during image de-noising such as smoothing the flat areas, safeguarding the edges without blurring, and keeping the textures and new artifacts should not be created. Block Matching, Updating of basis vector, Sparsity regularization, and TV regularization. This method searches for blocks that are comparable to each other in block matching. The data in the array demonstrates a high level of correlation after the matching blocks are grouped together. The sparse coefficients will be gathered after adequate modification. Most of the noise in the image will be minimized through the sparsity regularization step by employing different de-noising algorithms such as Block matching 3D using fixed basis vectors. The edge information will be retained and the piecewise smoothness of the image will be produced using the TV regularization step. Later, in the second state create a contour on the de-noised image and evolve the contour based on level Set function (LSF) defined. This combined approach gives better performance for segmenting the image regions over existing level set methods. When compared our proposed level set method over state of art level set methods. The proposed segmentation method is superior in terms of no.of iterations, CPU time and area covered over the existing level set methods. By this model, we obtained a good quality of restored image from noisy image and the performance of the image quality assessed by the two important parameters such as PSNR and Mean Square Error (MSE). The higher value of PSNR and lower value of MSE leads to good quality of image. In this research work, the proposed denoising method got higher PSNR values over existing methods. Where recovering the original image content is essential for effective performance, image denoising is a key component. It is used in a variety of applications, including image restoration, visual tracking, image registration, image segmentation, and image classification. This model is the best segmentation method for accurate segmentation of objects based on denoising images when compared with the other models in the field.