IDEAS home Printed from https://ideas.repec.org/a/bjf/journl/v11y2026i6p1956-1966.html

A Trustworthy Explainable Deep Learning Framework for Copy-Move Forgery Detection Using Multi-Level XAI Techniques

Author

Listed:
  • Shaheena K V

    (Research Scholar, Department of Computer Science, Sri Krishna Arts and Science College, Coimbatore)

  • Dhanalakshmi S

    (Associate Professor, Department of Software Systems & AIML, Sri Krishna Arts and Science College, Coimbatore)

Abstract

Copy-Move Forgery occurs when copies from different portions of images are placed at other locations to conceal or add content. This method is one of the most common and challenging types of digital manipulations. CNNs have shown great performance in detecting copy-move forgery; however, the issue with them lies in the fact that they are a “black-box.†Therefore, there is always suspicion about the results obtained by them in sensitive applications. We proposed an Explainable Artificial Intelligence (XAI) framework that uses our proposed detector alongside three well-known interpretability techniques: Grad-CAM, LIME, and SHAP. Our model not only correctly detects the forged region within the image but also offers understandable reasons behind the result obtained for the forgery location. Experiments on CASIA and CoMoFoD datasets show that we achieved 97.8%, 98.6%, 97.1%, and 97.8% accuracy, precision, recall, and F1-score, respectively, along with 0.72 IoU using Grad-CAM.

Suggested Citation

  • Shaheena K V & Dhanalakshmi S, 2026. "A Trustworthy Explainable Deep Learning Framework for Copy-Move Forgery Detection Using Multi-Level XAI Techniques," International Journal of Research and Innovation in Applied Science, International Journal of Research and Innovation in Applied Science (IJRIAS), vol. 11(6), pages 1956-1966, June.
    • Handle: RePEc:bjf:journl:v:11:y:2026:i:6:p:1956-1966
    as

    Download full text from publisher

    File URL: https://rsisinternational.org/journals/ijrias/uploads/vol11-iss6-pg1956-1966-202607_pdf.pdf
    Download Restriction: no
    File URL: https://rsisinternational.org/journals/ijrias/view/a-trustworthy-explainable-deep-learning-framework-for-copy-move-forgery-detection-using-multi-level-xai-techniques/
    Download Restriction: no
    ---><---

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bjf:journl:v:11:y:2026:i:6:p:1956-1966. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Dr. Renu Malsaria (email available below). General contact details of provider: https://rsisinternational.org/journals/ijrias/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.