{
  "abstract": "Deep learning models have revolutionized medical image analysis but remain vulnerable to adversarial attacks. This work presents FusionStack, an explainability-driven framework for detecting adversarial perturbations through multi-method XAI stability analysis. We introduce three novel stability metrics (LIME Stability Metric, SHAP Stability Metric, Grad-CAM Stability Metric) quantifying explanation consistency across convolutional feature hierarchies, augmented with frequency-domain and gradient-based features. Tested on five medical imaging datasets, five CNN models, and five adversarial attacks with rigorous patient-level separation, FusionStack achieves 98.71% accuracy and 0.998 AUC. Cross-attack evaluation shows DeepFool-trained detectors achieve 96.85% transferability as universal defenders. Cross-dataset evaluation demonstrates 87.05% mean accuracy, with histopathology-trained detectors achieving 100% transfer to all modalities. Lightweight frequency-domain detection (10 features) achieves 95.46% accuracy with $$5.75\\times$$ speedup. Our findings establish XAI stability analysis as a principled pathway toward adversarially robust medical AI systems for safety-critical applications.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Mathematical and Physical Sciences, East West University",
    "Department of Computer Science and Engineering, East West University"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 4,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Sudarshan Saha"
    },
    {
      "affs": [
        0
      ],
      "name": "Sohel Rana"
    },
    {
      "affs": [
        1
      ],
      "name": "Ahmed Wasif Reza"
    },
    {
      "affs": [
        0
      ],
      "name": "M. Ruhul Amin"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/cq4jn3w5tnts4.bib",
  "cited_by": 0,
  "doi": "10.1038/s41598-026-61357-1",
  "fulltext_url": "https://doi.org/10.1038/s41598-026-61357-1",
  "id": "cq4jn3w5tnts4",
  "issns": [
    "2045-2322"
  ],
  "landing_url": "https://doi.org/10.1038/s41598-026-61357-1",
  "language": "en",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "lcc",
      "Q"
    ],
    [
      "lcc",
      "R"
    ],
    [
      "openalex",
      "1702|Adversarial Robustness in Machine Learning"
    ]
  ],
  "openalex": "W7170164679",
  "pdf_url": null,
  "pmcid": null,
  "pmid": null,
  "published": "2026-07-01",
  "publisher": "Nature Portfolio",
  "ris": "https://research.draconitic.com/paper/cq4jn3w5tnts4.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T13:45:52Z",
      "native_id": "237696f47826477c8752ef2f5899d70e",
      "source": "doaj",
      "url": "https://doi.org/10.1038/s41598-026-61357-1"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "lcc:R",
      "subfield": "general"
    }
  ],
  "title": "Unmasking adversarial attacks using a robust XAI-driven approach for secure medical image classification",
  "title_text": "Unmasking adversarial attacks using a robust XAI-driven approach for secure medical image classification",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T13:45:52Z",
  "url": "https://research.draconitic.com/paper/cq4jn3w5tnts4",
  "venue": "Scientific Reports",
  "versions": []
}