IRAD: Implicit Representation-driven Image Resampling against Adversarial Attacks
Authors: Yue Cao, Tianlin Li, Xiaofeng Cao, Ivor Tsang, Yang Liu, Qing Guo
ICLR 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments demonstrate that our method significantly enhances the adversarial robustness of diverse deep models against various attacks while maintaining high accuracy on clean images. |
| Researcher Affiliation | Academia | 1 CFAR and IHPC, Agency for Science, Technology and Research (A*STAR), Singapore 2 School of Computer Science and Engineering, Nanyang Technological University, Singapore 3 Jilin University, China |
| Pseudocode | No | No pseudocode or clearly labeled algorithm block was found in the paper. |
| Open Source Code | Yes | We released our code in https://github.com/tsingqguo/irad. |
| Open Datasets | Yes | We use three datasets: CIFAR10 (Krizhevsky et al., a), CIFAR100 (Krizhevsky et al., b) and Image Net (Deng et al., 2009). |
| Dataset Splits | No | The paper mentions using training and testing datasets, for example, 'We train the Wide Res Net2810 on CIFAR10 dataset and calculate the clean testing dataset s accuracy'. However, it does not explicitly provide specific percentages or counts for training/validation/test splits, nor does it explicitly mention a 'validation' split. |
| Hardware Specification | Yes | These experiments were conducted using the AMD EPYC 7763 64-Core Processor with 1 NVIDIA A100 GPUs. |
| Software Dependencies | No | The paper mentions using Adam optimizer and L1/cross-entropy loss functions, but it does not specify software dependencies with version numbers (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | Yes | The PGD attack uses an ϵ value of 8/255 and 100 steps, with a step size 2/255. For implicit representation training, we use Adam as the optimizer with a learning rate of 1e-4 and betas of (0, 0.9) as parameters... training is conducted with a batch size of 128. For Sample Net training... learning rate of 2e-4 for CIFAR10 and Image Net, 1e-3 for CIFAR100, and betas set to (0, 0.9). The training of Sample Net is conducted with a batch size of 400 for CIFAR10, 200 for CIFAR100, and 8 for Image Net. |