Adversarial Training(AT) on long-tailed distributions triggers catastrophic vulnerability in tail classes. Prior work has predominantly relied on passive loss reweighting to compensate for data imbalance, leaving the underlying scarcity of tail-class samples unaddressed at the data level. In this work, we propose an active manifold-completion strategy that integrates conditional diffusion generation with self-supervised adversarial contrastive learning. Our frame-work introduces three core innovations. First, we exploit diffusion-based generative priors to repair the fragmented feature manifolds of minority classes by synthesizing on-manifold samples conditioned on tail-class labels. Second, we propose LT SSL AT, a dual-path self-supervised adversarial framework that decouples semantic invariance learning (clean-augmented views) from robustness learning (clean vs. adversarial pairs). Third, we introduce a curriculum scheduling weight s(t) that progressively shifts optimization from an Self-Supervised Learning(SSL)-only warm-up phase to joint adversarial alignment, preventing representation collapse. On CIFAR-10-LT (IR=10), CD-SSL achieves 35.12% Balanced Robustness under AutoAttack, surpassing the state-of-the-art TAET baseline by 4.58 percentage points. Extensive experiments across imbalance ratios 10, 20, 50, and 100 on three benchmarks (CIFAR-10-LT, CIFAR-100-LT, and MedMNIST) confirm consistent gains over both long-tailed and adversarial baselines.

The use of privacy-enhanced facial recognition has increased in response to growing concerns about data security and privacy in the digital age. This trend is spurred by rising demand for face recognition technology in a variety of industries, including access control, law enforcement, surveillance, and internet communication. However, the growing usage of face recognition technology has created serious concerns about data monitoring and user privacy preferences, especially in context-aware systems. In response to these problems, this study provides a novel framework that integrates sophisticated approaches such as Generative Adversarial Networks (GANs), Blockchain, and distributed computing to solve privacy concerns while maintaining exact face recognition. The framework’s painstaking design and execution strive to strike a compromise between precise face recognition and protecting personal data integrity in an increasingly interconnected environment. Using cutting-edge tools like Dlib for face analysis, Ray Cluster for distributed computing, and Blockchain for decentralized identity verification, the proposed system provides scalable and secure facial analysis while protecting user privacy. The study’s contributions include the creation of a sustainable and scalable solution for privacy-aware face recognition, the implementation of flexible privacy computing approaches based on Blockchain networks, and the demonstration of higher performance over previous methods. Specifically, the proposed StyleGAN model has an outstanding accuracy rate of 93.84% while processing high-resolution images from the CelebA-HQ dataset, beating other evaluated models such as Progressive GAN 90.27%, CycleGAN 89.80%, and MGAN 80.80%. With improvements in accuracy, speed, and privacy protection, the framework has great promise for practical use in a variety of fields that need face recognition technology. This study paves the way for future research in privacy-enhanced face recognition systems, emphasizing the significance of using cutting-edge technology to meet rising privacy issues in digital identity.

Nowadays, advancements in facial recognition technology necessitate robust solutions to address challenges in real-world scenarios, including lighting variations and facial position discrepancies. We introduce a novel deep neural network framework that significantly enhances facial recognition accuracy through multi-scale feature fusion and spatial attention mechanisms. Leveraging techniques from FaceNet and incorporating atrous spatial pyramid pooling and squeeze-excitation modules, our approach achieves superior accuracy, surpassing 99% even under challenging conditions. Through meticulous experimentation and ablation studies, we demonstrate the efficacy of each component, highlighting notable improvements in noise resilience and recall rates. Moreover, the introduction of the Feature Generative Spatial Attention Adversarial Network (FFSSA-GAN) model further advances the field, exhibiting exceptional performance across various domains and datasets. Looking forward, our research emphasizes the importance of ethical considerations and transparent methodologies in facial recognition technology, paving the way for responsible deployment and widespread adoption in the security, healthcare, and retail industries.