An Evolutionary Algorithm for Black-Box Adversarial Attack Against Explainable Methods
Authors: Phoenix Neale Williams, Jessica Schrouff, Lea Goetz
TMLR 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on medical and natural image datasets demonstrate that our method outperforms state-of-the-art techniques, exposing critical vulnerabilities in current XAI systems and highlighting the need for more robust interpretability frameworks. ... Section 4 presents the evaluation metrics and experimental results, followed by a comprehensive analysis. |
| Researcher Affiliation | Industry | Phoenix Williams EMAIL GSK.ai Jessica Schou! jessica.v.schrou!@gsk.com GSK.ai Lea Goetz EMAIL GSK.ai |
| Pseudocode | No | The paper describes the proposed method in Section 3 and states, "The overall structure of our approach is summarized in Figure 6 within the appendix." Figure 6 is a diagram, not pseudocode or an algorithm block. The methodology is described in prose. |
| Open Source Code | No | To encourage further research in this domain, we will publicly release our implementation, datasets, and evaluation scripts upon acceptance of the paper. |
| Open Datasets | Yes | The HAM10000 dataset Tschandl et al. (2018) contains approximately 10,000 dermatology images... The Br35h dataset Hamada (2020) consists of 3,000 brain MRI scans... The COVID-QU-Ex dataset Tahir et al. (2021) provides 33,920 chest X-rays... |
| Dataset Splits | Yes | The datasets are divided into training, validation, and testing subsets with a ratio of 70%/10%/20%. |
| Hardware Specification | Yes | All experiments were executed on an NVIDIA RTX A6000 GPU system. |
| Software Dependencies | No | Given the relatively small size of our medical datasets, we fine-tune models pre-trained on Image Net using the Py Torch library Paszke et al. (2019). No specific version number for PyTorch is provided, nor are other software dependencies mentioned with versions. |
| Experiment Setup | Yes | Each model undergoes fine-tuning over 10 epochs, with a batch size of 32 and a learning rate of 1 × 10−4, utilizing the ADAM optimizer Kingma & Ba (2015) and cross entropy loss. ... our approach involves three adjustable parameters: ε, N, and Max Diameter . The specific values for these parameters are listed in Table 4, with justification provided in Section 4.5. |