One-bit Flip is All You Need: When Bit-flip Attack Meets Model Training
We propose a training-assisted bit flip attack, in which the adversary is involved in the training stage to build a high-risk model to release.
Tags：Paper and LLMsPricing Type
- Pricing Type: Free
- Price Range Start($):
GitHub Link
The GitHub link is https://github.com/jianshuod/tba
Introduce
The GitHub repository “jianshuod/TBA” contains the official code for the ICCV 2023 paper titled “One-bit Flip is All You Need When Bit-flip Attack Meets Model Training.” The project focuses on the convergence of bit-flip attacks and model training. The code, developed using Python 3 and PyTorch, offers a main pipeline for the method and provides instructions for installation and usage. The repository includes details about task specifications, hyperparameters, and results, particularly highlighting the attacking of 8-bit quantized ResNet-18. The work is licensed under the Apache License 2.0.
We propose a training-assisted bit flip attack, in which the adversary is involved in the training stage to build a high-risk model to release.
Content
This is the official implementation of our paper One-bit Flip is All You Need: When Bit-flip Attack Meets Model Training, accepted by ICCV 2023. This research project is developed based on Python 3 and Pytorch. If you think this work or our codes are useful for your research, please cite our paper via: Install by running the following cmd in the work directory Step 1: Download the model checkpoint, and then place it in the directory “checkpoint/resnet18” Step 2: Fill out the path to this work directory in your server Step 3: configure the path to CIFAR-10 dataset in config.py The log for attacking 8-bit quantized ResNet-18 is provided. Please refer to log_resnet18_8.txt for our results. This project is licensed under the terms of the Apache License 2.0. See the LICENSE file for the full text.
Related
In the pursuit of promoting the expressiveness of GNNs for tail nodes, we explore how the deficiency of structural information deteriorates the performance of tail nodes and propose a general Structural Augmentation based taIL nOde Representation learning framework, dubbed as SAILOR, which can jointly learn to augment the graph structure and extract more informative representations for tail nodes.
