DISCO: Adversarial Defense with Local Implicit Functions
Authors: Chih-Hui Ho, Nuno Vasconcelos
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments demonstrate that both DISCO and its cascade version outperform prior defenses, regardless of whether the defense is known to the attacker. DISCO is also shown to be data and parameter efficient and to mount defenses that transfers across datasets, classifiers and attacks. |
| Researcher Affiliation | Academia | Chih-Hui Ho Nuno Vasconcelos Department of Electrical and Computer Engineering University of California, San Diego {chh279, nvasconcelos}@ucsd.edu |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. It describes the architecture and training procedures in text and diagrams. |
| Open Source Code | Yes | Code availabe at https://github.com/chihhuiho/disco.git |
| Open Datasets | Yes | Three datasets are considered: Cifar10 [58], Cifar100 [59] and Imagenet [23]. |
| Dataset Splits | No | The paper mentions "training pairs" and "evaluation on the test set" but does not explicitly describe or specify a separate "validation" dataset split or how it was used. |
| Hardware Specification | Yes | All experiments are conducted on a single Nvidia Titan Xp GPU with Intel Xeon CPU E5-2630 using Pytorch [85]. |
| Software Dependencies | No | The paper mentions "Pytorch [85]" but does not specify a version number for it or any other software dependency, which is required for reproducibility. |
| Experiment Setup | Yes | The network is then trained to minimize the L1 loss between the predicted RGB value and that of the clean xcln and defense output xdef. By default, the kernel size s is set to be 3. Random patches of size 48x48 are sampled from training pairs. |