Constructing Semantics-Aware Adversarial Examples with a Probabilistic Perspective
Authors: Andi Zhang, Mingtian Zhang, Damon Wischik
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 6 Experiments, Table 1: Success rate (%) of the methods on MNIST., Table 2: Success rate (%) of the methods on Imagenet. |
| Researcher Affiliation | Academia | Andi Zhang Computer Laboratory University of Cambridge az381@cantab.ac.uk, Mingtian Zhang Centre for Artificial Intelligence University College London m.zhang@cs.ucl.ac.uk, Damon Wischik Computer Laboratory University of Cambridge djw1005@cam.ac.uk |
| Pseudocode | Yes | Algorithm 1 Sampling from padv by EBM, Algorithm 2 Sampling from padv by diffusion model, Algorithm 3 Rejection Sampling and Sample Refinement, Algorithm 4 Sampling from padv by diffusion model |
| Open Source Code | Yes | Code can be found at https://github.com/andiac/ Adv PP. |
| Open Datasets | Yes | We use MNIST [25] and Image Net [9] in this work. The MNIST dataset is available under the terms of the Creative Commons Attribution-Share Alike 3.0 license. |
| Dataset Splits | No | The paper mentions using images from the MNIST test set and refers to adversarially trained models, but does not explicitly detail the train/validation splits used for its own experiments. |
| Hardware Specification | Yes | We conducted our experiments using multiple workstations, each equipped with an NVIDIA RTX 4090 GPU (24GB VRAM) and 64GB of system memory. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., Python, PyTorch, or CUDA versions). |
| Experiment Setup | Yes | Our method is evaluated across three hyperparameter configurations: c = 5, c = 10, and c = 20. |