An efficient semi-blind watermarking technique based on ACM and DWT for mitigating integrity attacks
Published in: Arabian Journal for Science and Engineering
Oct 15, 2025
Digital watermarking is an essential technology in multimedia and information processing, addressing the ever-mounting concerns related to data integrity and protecting intellectual property rights. In content authentication, copyright protection, and data integrity, digital watermarking plays a critical role. Nonetheless, the current application of digital watermarking faces crucial challenges, most notably adversarial attacks such as compression and noise interference, which pose substantial ...
LENS: Lightweight and Explainable LLM-Based APT Detection at the Edge for 6G Security
Published in: IEEE Access
Sep 30, 2025
Expected to be deployed in the early 2030s, sixth-generation (6G) wireless networks, with their high speed and integration with cutting-edge technology such as intelligent edge computing, expand the attack surface and face serious cyber threat risks such as Advanced Persistent Threats (APTs). This type of cyber attack can imitate benign network traffic and operate for long periods of time without being detected by traditional detection systems. This paper introduces LENS, a lightweight and expl...
An OTP and watermarking based partial homomorphic approach to authenticated and integrated cloud computing
Published in: Cluster Computing
Sep 24, 2025
Cloud computing offers an ideal solution for reducing costs, time, and complexity in data management, but data security remains a critical concern. This study proposes a novel approach that combines partial homomorphic encryption (PHE) with digital watermarking to improve the authenticity and integrity of data in cloud environments. Our method employs a chaotic map for key generation, ensuring unique and non-duplicate keys, and utilizes the One-Time Pad (OTP) principle with probabilistic encryp...
Optimizing Fetal Health Diagnosis: An Active Learning Framework with LightGBM
Published in: 2025 Sixth International Conference on Intelligent Data Science Technologies and Applications (IDSTA)
Sep 01, 2025
Sana Fatima
Mamoona Akbar
Haroon Mahmood
Anosha Khan
Fetal health classification is crucial for the timely identification of abnormalities and the improvement of neonatal care. Early prediction of fetal health is necessary to ensure a healthy pregnancy and lower rates of maternal and newborn mortality. Machine learning algorithms improve fetal health monitoring by enabling early detection of abnormalities and facilitating timely medical interventions. However, traditional machine learning models rely on large labeled datasets, which are often cos...