Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].
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 | Venue PDF | 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 EMAIL, EMAIL, EMAIL |
| 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. |