Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Constructing Unrestricted Adversarial Examples with Generative Models
Authors: Yang Song, Rui Shu, Nate Kushman, Stefano Ermon
NeurIPS 2018 | Venue PDF | LLM Run Details | Input Tokens: 17,435 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,524 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our empirical results on the MNIST, SVHN, and Celeb A datasets show that unrestricted adversarial examples can bypass strong adversarial training and certi๏ฌed defense methods designed for traditional adversarial attacks. |
| Researcher Affiliation | Collaboration | Yang Song Stanford University EMAIL Rui Shu Stanford University EMAIL Nate Kushman Microsoft Research EMAIL Stefano Ermon Stanford University EMAIL |
| Pseudocode | Yes | In what follows, we explore two attacks derived from variants of AC-GAN (see pseudocode in Appendix B). |
| Open Source Code | No | The paper does not contain any explicit statement or link indicating the availability of the source code for the described methodology. |
| Open Datasets | Yes | The datasets used in our experiments are MNIST [25], SVHN [26], and Celeb A [27]. |
| Dataset Splits | No | The paper mentions using training and test partitions but does not provide specific percentages or counts for training, validation, and test splits needed for reproduction, nor does it refer to a standard split that quantifies all partitions. |
| Hardware Specification | No | The paper does not specify the exact models or types of hardware (e.g., GPUs, CPUs, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions software components like TensorFlow (Appendix C: 'We implement all models in TensorFlow.') but does not provide specific version numbers for these or any other key libraries or solvers. |
| Experiment Setup | Yes | For more details about architectures, hyperparameters and adversarial training methods, please refer to Appendix C. |