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.
74 matching profile project records · Page 6 of 7
developed a machine-learning framework for detecting and mitigating data-poisoning attacks.
Java/Spring Boot prototype for food-product APIs, QR-code generation and basic payment-transaction records, using configurable H2/MySQL storage.
a Python/Ollama prototype that checks syntax, generates code corrections and retries following execution errors.
a policy-document question-answering prototype combining risk detection, adaptive retrieval, answer evaluation, evidence checks and retrieval repair.
the published study investigates inference-time backdoor-sample detection through internal stability analysis.
React and Material UI interview-management prototype with interview creation, expert listings, search and a skill-discussion interface.
Primary developer of Towerdefense, a Mindustry server plugin with configurable waves, shops, an in-game economy and gameplay-tuning systems.
Python and FastAPI project for PDF extraction, Gemini-assisted translation and layout reconstruction, with command-line and web interfaces.
contributed to a Q-learning-based routing system for underwater software-defined networks.
public FastAPI training and prediction pipeline with MongoDB ingestion, S3 model storage and Docker packaging.
developed a CNN/OCR pipeline in Python using OpenCV, imutils and Tesseract for traffic-monitoring images.
Built encrypted-traffic and VPN research software for collection coordination, traffic preparation, feature extraction, model training, evaluation and experiment provenance, using third-party tools and libraries. The work includes traffic contributed by Ayush Kumar and volunteers during everyday device use, kept distinct from imported benchmarks and synthetic examples. The study has documented consent and institutional ethics approval. Its public Android companion uses the PCAPdroid capture engine. A truncated public data edition is prepared locally and awaits publication; raw research access for trusted researchers and organisations is considered through enquiry and review.
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.