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..
GSE: Group-wise Sparse and Explainable Adversarial Attacks
Authors: Shpresim Sadiku, Moritz Wagner, Sebastian Pokutta
ICLR 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Rigorous evaluations on CIFAR-10 and Image Net datasets demonstrate a remarkable increase in groupwise sparsity, e.g., 50.9% on CIFAR-10 and 38.4% on Image Net (average case, targeted attack). This performance improvement is accompanied by significantly faster computation times, improved explainability, and a 100% attack success rate. |
| Researcher Affiliation | Academia | Shpresim Sadiku1,2, Moritz Wagner1,2 & Sebastian Pokutta1,2 1Department for AI in Society, Science, and Technology, Zuse Institute Berlin, Germany 2Institute of Mathematics, Technische Universität Berlin, Germany EMAIL |
| Pseudocode | Yes | Algorithm 1 Forward-Backward Splitting Attack |
| Open Source Code | Yes | All tests are conducted on a machine with an NVIDIA A40 GPU, and our codes, 10k image indices from the Image Net validation dataset, and target labels for targeted Image Net tests are available at https://github.com/wagnermoritz/GSE. |
| Open Datasets | Yes | We experiment on CIFAR-10 (Krizhevsky et al., 2009) and Image Net (Deng et al., 2009) datasets, analyzing DNNs on 10k randomly selected images from both validation sets. |
| Dataset Splits | Yes | We experiment on CIFAR-10 (Krizhevsky et al., 2009) and Image Net (Deng et al., 2009) datasets, analyzing DNNs on 10k randomly selected images from both validation sets. For the classifier C on CIFAR-10, we train a Res Net20 model (He et al., 2016) for 600 epochs using stochastic gradient descent, with an initial learning rate of 0.01, reduced by a factor of 10 after 100, 250, and 500 epochs. We set the weight decay to 10 4, momentum to 0.9, and batch size to 512. For Image Net, we employ a Res Net50 (He et al., 2016) and a more robust transformer model, Vi T_B_16 (Dosovitskiy et al., 2020), both with default weights from the torchvision library. |
| Hardware Specification | Yes | All tests are conducted on a machine with an NVIDIA A40 GPU |
| Software Dependencies | No | The paper mentions "Py Torch" in Section 3.3, but does not specify a version number. |
| Experiment Setup | Yes | For the classifier C on CIFAR-10, we train a Res Net20 model (He et al., 2016) for 600 epochs using stochastic gradient descent, with an initial learning rate of 0.01, reduced by a factor of 10 after 100, 250, and 500 epochs. We set the weight decay to 10 4, momentum to 0.9, and batch size to 512. Specifically, for CIFAR-10, we set q = 0.25, σ = 0.005, µ = 1, and ˆk = 30, while for Image Net, we use q = 0.9, σ = 0.05, µ = 0.1, and ˆk = 50. We run all the attacks for a total of 200 iterations. |