Adversarial vulnerability for any classifier
Authors: Alhussein Fawzi, Hamza Fawzi, Omar Fawzi
NeurIPS 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conclude with numerical experimental results showing that our bounds provide informative baselines to the maximal achievable robustness on several datasets. We evaluate our bounds in several experimental setups (CIFAR-10 and SVHN), and show that they yield informative baselines to the maximal achievable robustness. |
| Researcher Affiliation | Collaboration | Alhussein Fawzi Deep Mind afawzi@google.com Hamza Fawzi Department of Applied Mathematics & Theoretical Physics University of Cambridge h.fawzi@damtp.cam.ac.uk Omar Fawzi ENS de Lyon omar.fawzi@ens-lyon.fr |
| Pseudocode | No | The paper does not contain any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | The paper does not provide any explicit statements or links indicating that open-source code for the described methodology is available. |
| Open Datasets | Yes | We now evaluate our bounds on the SVHN dataset [37] which contains color images of house numbers... We now consider the more complex CIFAR-10 dataset [39]. |
| Dataset Splits | No | The paper specifies training and test image counts for SVHN (73,257 training, 26,032 test) but does not explicitly mention a separate validation split or its size/percentage. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used to run the experiments, such as GPU models or CPU specifications. |
| Software Dependencies | No | The paper mentions using a DCGAN model and neural network architectures, but it does not specify any software names with version numbers for reproducibility (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | Yes | We train a DCGAN [30] generative model on this dataset, with a latent vector dimension d = 100, and further consider several neural networks architectures for classification. |