Towards Imperceptible and Robust Adversarial Example Attacks Against Neural Networks
Authors: Bo Luo, Yannan Liu, Lingxiao Wei, Qiang Xu
AAAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results demonstrate the efficacy of the proposed technique. and Experimental Evaluations Dataset. All the experiments are performed on MNIST and CIFAR10 datasets. ... DNN Model. For each dataset, we trained a model. ... Baselines. The baselines used in these experiments are three widely-used adversarial example attacks... |
| Researcher Affiliation | Academia | Bo Luo, Yannan Liu, Lingxiao Wei, Qiang Xu Department of Computer Science & Engineering The Chinese University of Hong Kong {boluo,ynliu,lxwei,qxu}@cse.cuhk.edu.hk |
| Pseudocode | Yes | Algorithm 1: The proposed algorithm to generate adversarial examples. |
| Open Source Code | No | The paper does not provide any concrete access information (link, explicit statement of release) to open-source code for the described methodology. |
| Open Datasets | Yes | Dataset. All the experiments are performed on MNIST and CIFAR10 datasets. The MNIST dataset (Le Cun, Cortes, and Burges 2010) includes 70000 gray scale hand-written digit images... The CIFAR10 dataset (Krizhevsky, Nair, and Hinton 2014) contains 6000 color images. |
| Dataset Splits | No | We perform adversarial example attacks against the testing set (10000 test images) in MNIST and CIFAR10 respectively. The paper mentions the testing set size but does not specify the training/validation splits or their sizes. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions deep neural networks and common functions but does not specify any software names with version numbers for reproducibility. |
| Experiment Setup | Yes | In our method, we select 20 pixels to add perturbations with a magnitude of 0.01 in each iteration. |