Backpropagating Linearly Improves Transferability of Adversarial Examples
Authors: Yiwen Guo, Qizhang Li, Hao Chen
NeurIPS 2020 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results demonstrate that this simple yet effective method obviously outperforms current state-of-the-arts in crafting transferable adversarial examples on CIFAR-10 and Image Net, leading to more effective attacks on a variety of DNNs. |
| Researcher Affiliation | Collaboration | Yiwen Guo Byte Dance AI Lab EMAIL Qizhang Li Byte Dance AI Lab EMAIL Hao Chen University of California, Davis EMAIL |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code at: https://github.com/qizhangli/linbp-attack. |
| Open Datasets | Yes | We focus on untargeted ℓ attacks on deep image classifiers. Different methods are compared on CIFAR-10 [27] and Image Net [41]... |
| Dataset Splits | No | The paper mentions using 5000 test instances for evaluation and refers to test sets, but it does not explicitly provide details about a validation dataset split or its purpose for hyperparameter tuning. |
| Hardware Specification | Yes | All experiments are performed on an NVIDIA V100 GPU with code implemented using Py Torch [39]. |
| Software Dependencies | No | The paper mentions that code was implemented using 'Py Torch [39]' but does not provide a specific version number for PyTorch or any other software dependency. |
| Experiment Setup | Yes | On both datasets, we set the maximum perturbation as ϵ = 0.1, 0.05, 0.03 to keep inline with ILA. ... we run for 100 iterations on CIFAR-10 inputs and 300 iterations on Image Net inputs with a step size of 1/255 such that its performance reaches plateaus on both datasets. |