Poison Frogs! Targeted Clean-Label Poisoning Attacks on Neural Networks
Authors: Ali Shafahi, W. Ronny Huang, Mahyar Najibi, Octavian Suciu, Christoph Studer, Tudor Dumitras, Tom Goldstein
NeurIPS 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate our method by generating poisoned frog images from the CIFAR dataset and using them to manipulate image classifiers. |
| Researcher Affiliation | Academia | Ali Shafahi University of Maryland ashafahi@cs.umd.edu W. Ronny Huang University of Maryland wrhuang@umd.edu Mahyar Najibi University of Maryland najibi@cs.umd.edu Octavian Suciu University of Maryland osuciu@umiacs.umd.edu Christoph Studer Cornell University studer@cornell.edu Tudor Dumitras University of Maryland tudor@umiacs.umd.edu Tom Goldstein University of Maryland tomg@cs.umd.edu |
| Pseudocode | Yes | Algorithm 1 Poisoning Example Generation |
| Open Source Code | Yes | 2The code is available at https://github.com/ashafahi/inceptionv3-transferLearn-poison |
| Open Datasets | Yes | We used the Adam optimizer with learning rate of 0.01 to train the network for 100 epochs. |
| Dataset Splits | No | The paper provides details on training and test sets but does not explicitly mention a separate validation set split or its details. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, processor types, or memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper does not specify any particular software versions or dependencies required to replicate the experiment. |
| Experiment Setup | Yes | We use the Adam optimizer with learning rate of 0.01 to train the network for 100 epochs. |