Trustworthy Artificial Intelligence Course Notebooks, 2023
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Updated
Mar 1, 2024 - Jupyter Notebook
Trustworthy Artificial Intelligence Course Notebooks, 2023
PyTorch implementation of “Conditional Adversarial Camera Model Anonymization” (ECCV 2020 Advances in Image Manipulation Workshop)
Code for our USENIX Security '22 paper: Transferring Adversarial Robustness Through Robust Representation Matching.
Preprocessed the USPS dataset, implemented and compared different network architectures and optimization techniques, applied regularization techniques such as ensembling and dropout, performed adversarial training to evaluate network robustness, and evaluated network performance using metrics such as accuracy, precision, and recall.
On The Impact of Adversarial Training and Transferability on CAN Intrusion Detection Systems
Combating robust overfitting in adversarial training via AdvLC
[ECCV 2022] The official repository of ''$\ell_\infty$-Robustness and Beyond: Unleashing Efficient Adversarial Training''.
A classical or convolutional neural network model with adversarial defense protection
An adversarial training for ReColorAdv attack.
Instance adaptive Smoothness Enhanced Adversarial Training (ISEAT)
Adversarial Style for Image Classification
Code for ARCH: Adversarial Regularization with Caching, Findings of EMNLP 2021.
A PyTorch Based Deep Learning Quick Develop Framework. One-Stop for train/predict/server/demo
My fundamental topics - research on Adversarial machine learning
some paper of Knowledge Distillation and Adversarial Training about NLP
data augmentation alone can improve adversarial training
LFRC: Latent Feature Relation Consistency for Adversarial Robustness
Hybrid neural network is protected against adversarial attacks using various defense techniques, including input transformation, randomization, and adversarial training.
Evaluating the Use of Fast Adversarial Training in Defending Against Adversarial Patch Attacks
Textual adversarial training with textattack
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