Image forgery detection techniques have evolved into ... Deep learning: A subset of machine learning employing neural networks with multiple layers to autonomously extract high-level features ...

Image forgery detection ... to better target subtle forgery traces in complex images [5]. These contributions underscore the relevance of advanced machine learning methods in ensuring the veracity ...

A new machine learning tool by Trugard and Webacy targets crypto wallet address poisoning, with the companies reporting 97% detection accuracy.

Traditional image forgery detection methods face challenges in reproducing ... embedded within images to detect tampering or alterations. Image Forensics using Machine Learning Employing machine ...

Thanks to recent breakthroughs in machine learning, he had shifted focus to models that could detect the kinds of visual inconsistencies typical of an AI-generated image: oddities of perspective and ...

This research addresses the present issue of photo forgery by proposing a robust detection ... features descriptorbased methods have been used to detect imminent features of the images, then machine ...

IDs continue to be at risk from counterfeiting but optical technologies play a central role in the battle to protect people ...

Budoen, A. , Zhang, M. and Jr., L. (2025) A Comparative Study of Ensemble Learning Techniques and Classification Models to Identify Phishing Websites. Open Access Library Journal, 12, 1-22. doi: ...

SAVANA uses a machine learning algorithm to identify cancer-specific structural variations and copy number aberrations in long-read DNA sequencing data. The complex structure of cancer genomes means ...

Regula is upgrading its popular video spectral comparator, the 4306M, to keep pace with the complexities of document ...

Agentic AI can enable us to move beyond static controls and embrace a responsive, risk-aware model of identity governance.