Security of Neural Networks
Defending deep models against adversarial examples, backdoor and data-poisoning attacks — and understanding when and why they fail.
Research studies, software and prototypes from across the NPerseus team.
Research studies, prototypes and software described in public profiles. Status, roles and funding follow each source record; a profile entry does not by itself establish a released product.
Applications, tools, plugins and prototypes members have built. Engineering evidence, kept apart from the research record.
42 matching software project records · Page 2 of 4
circular timer with notification-area play/pause controls.
prioritized notes, Sqflite storage and persistent dark theme.
Java and Spring Boot developer tool for retrieving public repositories and inspecting source structure with configurable heuristic complexity scores.
Built developer tooling for grid-based code-golf tasks, including a task browser, solution comparison, example-based validation and code-size analysis, using third-party tools and libraries.
built an end-to-end regression pipeline with pandas, scikit-learn and gradient boosting, exposed through a FastAPI service.
developed NodeMCU-based home automation and an LPG/smoke leakage detector.
Primary developer of Kartikeya, a Mindustry server-management plugin covering Discord integration, administration, voting, map workflows and player history.
co-announced a collaborative project focused on accessible conversational practice.
experimental Raspberry Pi/Linux driver project for light-based data communication, published as Laser-Driver.
a prototype retrieving PubMed literature and generating answers with retrieved sources.
developed a PHP and MySQL web portal for donating unused medicines.
Defending deep models against adversarial examples, backdoor and data-poisoning attacks — and understanding when and why they fail.
Reversible data hiding in encrypted images, image and text steganography, and robust watermarking for ownership and integrity.
Detecting manipulated and GAN-generated media — deepfakes, splicing and median-filtering traces.
Federated and privacy-preserving learning, explainable and trustworthy models, and methods for limiting the disclosure of training data.