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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Functional Adversarial Attacks
Authors: Cassidy Laidlaw, Soheil Feizi
NeurIPS 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We experiment by attacking defended and undefended classifiers with Re Color Adv, by itself and in combination with other attacks. We find that Re Color Adv is a strong attack, reducing the accuracy of a Res Net-32 trained on CIFAR-10 to 3.0%. |
| Researcher Affiliation | Academia | Cassidy Laidlaw University of Maryland EMAIL Soheil Feizi University of Maryland EMAIL |
| Pseudocode | No | The paper describes the methods and optimization process using textual descriptions and mathematical formulations, but it does not include structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | We release our code at https://github.com/cassidylaidlaw/Re Color Adv. |
| Open Datasets | Yes | We evaluate Re Color Adv against defended and undefended neural networks on CIFAR-10 [13] and Image Net [20]. |
| Dataset Splits | No | The paper uses standard datasets like CIFAR-10 and Image Net but does not explicitly state the training, validation, or test split percentages or sample counts in the main text. |
| Hardware Specification | Yes | All experiments were run on NVIDIA V100 GPUs. |
| Software Dependencies | No | The paper mentions using 'PyTorch [19]' and 'Adam optimizer [12]' but does not provide specific version numbers for these software dependencies. |
| Experiment Setup | Yes | We use the standard Adam optimizer [12] with a learning rate of 0.001. |