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 5 of 7
public TensorFlow/Keras notebook implementing ResUNet segmentation with focal Tversky loss.
the published framework combines model signals to detect poisoned samples and correct their labels.
explored binary translation and compiler tooling using Rust and LLVM.
built and maintains a full-stack research-group platform for member profiles, publications, projects and collaboration workflows.
trained and evaluated a transfer-learning CNN for chest X-ray classification.
the published study evaluates deep-learning network intrusion-detection systems while preserving protocol semantics.
experimental PlayStation 2 binary translator built with Rust and LLVM tooling.
Developing the lab at NIT Delhi; the official laboratory-development record lists 2026.
built a MERN application that uses CNN, MobileNetV2 and ResNet models to classify sketches from the Google QuickDraw dataset.
Java Mindustry plugin for automatic item and liquid replenishment using cached building requirements; developed from an earlier JavaScript replenishment mod, with RapidNew and Radpid-MindustryPlugin counted as one project family.
built a web application for discovering and managing recipes.
applied contrastive learning to spectral-spatial feature extraction and hyperspectral cube classification.
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.