Perceptual-Sensitive GAN for Generating Adversarial Patches
Authors: Aishan Liu, Xianglong Liu, Jiaxin Fan, Yuqing Ma, Anlan Zhang, Huiyuan Xie, Dacheng Tao1028-1035
AAAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments under semi-whitebox and black-box settings on two large-scale datasets GTSRB and Image Net demonstrate that the proposed PS-GAN outperforms state-of-the-art adversarial patch attack methods. |
| Researcher Affiliation | Academia | State Key Laboratory of Software Development Environment, Beihang University, China Department of Computer Science and Technology, University of Cambridge, UK UBTECH Sydney AI Centre, SIT, FEIT, University of Sydney, Australia {liuaishan, xlliu, jxfan, mayuqing, zal1506}@buaa.edu.cn, hx255@cam.ac.uk, dacheng.tao@sydney.edu.au |
| Pseudocode | Yes | Algorithm 1 Perceptual-Sensitive Generative Adversarial Network (PS-GAN). |
| Open Source Code | No | The paper does not provide any explicit statement about releasing source code or a link to a code repository. |
| Open Datasets | Yes | Extensive experiments are conducted on GTSRB (Houben et al. 2008) and Image Net (Deng et al. 2009)... we choose Quick Draw (J. Jongejan and Fox-Gieg. 2016) as the corresponding patch dataset. |
| Dataset Splits | Yes | Each image and patch is normalized to [ 1, 1] and scaled to 128x128x3 and 16x16x3, respectively. |
| Hardware Specification | Yes | In our experiments, we use Tensorflow and Keras for the implementation and test them on a NVIDIA Tesla K80 GPU cluster. |
| Software Dependencies | No | The paper mentions 'Tensorflow and Keras' but does not specify their version numbers, which are required for a reproducible description of software dependencies. |
| Experiment Setup | Yes | We train PS-GAN for 250 epochs with a batch size of 64, with the learning rate of 0.0002, decreased by 10% every 900 steps. As for the hyperparameters in loss function, we set λ range from 0.002 to 0.005 and γ to 1.0 and δ to 0.0001, respectively. |