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.
Fooling a Complete Neural Network Verifier
Authors: Dániel Zombori, Balázs Bánhelyi, Tibor Csendes, István Megyeri, Márk Jelasity
ICLR 2021 | Venue PDF | LLM Run Details | Input Tokens: 22,706 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 5,276 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 | Empirical evaluation shows that the attack is successful. We evaluated MIPVerify experimentally using two commercial solvers: Gurobi (Gurobi, 2020), and CPLEX (CPLEX, 2020), and the open source GLPK (GLPK, 2020). During these evaluations, we experimented with different values of σ and ω to see whether our adversarial networks could fool the MIPVerify approach. |
| Researcher Affiliation | Academia | D aniel Zombori, Bal azs B anhelyi, Tibor Csendes, Istv an Megyeri, M ark Jelasity Institute of Informatics, University of Szeged, Hungary EMAIL |
| Pseudocode | No | The paper includes network diagrams (Figure 1, Figure 2, Figure 3) to illustrate the adversarial networks but does not provide any formal pseudocode or algorithm blocks. |
| Open Source Code | Yes | The code is shared at https://github.com/szegedai/nn_backdoor. |
| Open Datasets | Yes | We will work with the MNIST dataset and we fix the backdoor pattern to be the top left pixel being larger than 0.05 (assuming the pixels are in [0, 1]). |
| Dataset Splits | No | The paper mentions evaluating on the 'test set of the MNIST dataset' but does not specify training, validation, or other explicit dataset splits (e.g., percentages or sample counts) needed for full reproducibility of data partitioning. |
| Hardware Specification | Yes | CPU: Intel(R) Xeon(R) CPU E5-2660 v4 @ 2.00GHz |
| Software Dependencies | Yes | Julia version 1.5.0; Gurobi: Gurobi Optimizer version 9.0.2 build v9.0.2rc0 (linux64); Gurobi julia package: Gurobi v0.8.1; CPLEX: IBM(R) ILOG(R) CPLEX(R) Interactive Optimizer 12.10.0.0; CPLEX julia package: CPLEX v0.6.6; GLPK julia package: GLPK v0.13.0, GLPKMath Prog Interface v0.5.0; MIPVerify julia package: MIPVerify v0.2.3; Ju MP julia package: Ju MP v0.18.6, Conditional Ju MP v0.1.0; Math Prog Base julia package: Math Prog Base v0.7.8 |
| Experiment Setup | Yes | We randomly generated 500 values for σ from the interval [−15, 2] and for all the sampled σ values we tested ω values 254, 255, . . . , 270. For our evaluation, we selected an MNIST classifier described in Wong & Kolter (2018) and used in (Tjeng et al., 2019) to evaluate MIPVerify. We will refer to this network as WK17a. It has two convolutional layers (stride length: 2) with 16 and 32 filters (size: 4 × 4) respectively, followed by a fully-connected layer with 100 units. All these layers use Re LU activations. |