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.
Optimal Strategies Against Generative Attacks
Authors: Roy Mor, Erez Peterfreund, Matan Gavish, Amir Globerson
ICLR 2020 | Venue PDF | LLM Run Details | Input Tokens: 34,949 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 6,384 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 | We evaluate the method empirically, showing that it outperforms existing methods in terms of resilience to generative attacks, and that it can be used effectively for data-augmentation in the few-shot learning setting. |
| Researcher Affiliation | Academia | Roy Mor Tel Aviv University Tel Aviv, Israel Erez Peterfreund The Hebrew University of Jerusalem Jerusalem, Israel Matan Gavish The Hebrew University of Jerusalem Jerusalem, Israel Amir Globerson Tel Aviv University Tel Aviv, Israel |
| Pseudocode | No | The paper describes mathematical proofs and theoretical models but does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our implementation is available at https://github.com/ roymor1/Optimal Strategies Against Generative Attacks. |
| Open Datasets | Yes | We next evaluate GIM in an authentication setting on two datasets: the Vox Celeb2 faces dataset (Nagrani et al., 2017; Chung & Zisserman, 2018), and the Omniglot handwritten character dataset (Lake et al., 2015). |
| Dataset Splits | Yes | We used the original split of 5994 identities for training and 118 for test. For Omniglot, We use the splits and augmentations suggested by Vinyals et al. (2016) and used by Snell et al. (2017). |
| Hardware Specification | No | The paper does not explicitly describe the specific hardware (e.g., GPU, CPU models, memory) used to run its experiments. |
| Software Dependencies | No | The paper mentions using 'Adam optimizer' and refers to specific loss functions and architectural inspirations from other papers, but it does not provide specific version numbers for software dependencies or libraries (e.g., PyTorch version, TensorFlow version, Adam implementation version). |
| Experiment Setup | Yes | Each experiment is trained for 200K iterations with a batch size of 4000 using the Adam optimizer (Kingma & Ba, 2015) with learning rate 10 4. The experiments on Omniglot were trained for 520k iterations with batch size 128 using the Adam optimizer (Kingma & Ba, 2015) with learning rate 10 6 for D, 10 5 for G, and 10 7 for MLPG... The experiments on Voxceleb2 were trained for 250k iterations with batch size 64 using the Adam optimizer (Kingma & Ba, 2015) with learning rate 10 4 for both D and G and 10 6 for MLPG. The regularization parameter was set to 10. |