| Texto completo | |
| Autor(es): |
Neves, Miguel Jose Das
;
Mahlow, Felipe Rodrigues Perche
;
de Souza, Renato Dias
;
Hernandes, Paulo Roberto G.
;
Brega, Jose Remo Ferreira
;
da Costa, Kelton Augusto Pontara
Número total de Autores: 6
|
| Tipo de documento: | Artigo Científico |
| Fonte: | JOURNAL OF IMAGING; v. 11, n. 11, p. 14-pg., 2025-11-18. |
| Resumo | |
This paper addresses the critical challenge of detecting content-aware image manipulations, specifically focusing on seam carving forgery. While deep learning models, particularly Convolutional Neural Networks (CNNs), have shown promise in this area, their black-box nature limits their trustworthiness in high-stakes domains like digital forensics. To address this gap, we propose and validate a framework for interpretable forgery detection, termed E-XAI (Ensemble Explainable AI). Conceptually inspired by Ensemble Learning, our framework's novelty lies not in combining predictive models, but in integrating a multi-perspective ensemble of explainability techniques. Specifically, we combine SHAP for fine-grained, pixel-level feature attribution with Grad-CAM for region-level localization to create a more robust and holistic interpretation of a single, custom-trained CNN's decisions. Our approach is validated on a purpose-built, balanced, binary-class dataset of 10,300 images. The results demonstrate high classification performance on an unseen test set, with a 95% accuracy and a 99% precision for the forged class. Furthermore, we analyze the model's robustness against JPEG compression, a common real-world perturbation. More importantly, the application of the E-XAI framework reveals how the model identifies subtle forgery artifacts, providing transparent, visual evidence for its decisions. This work contributes a robust end-to-end pipeline for interpretable image forgery detection, enhancing the trust and reliability of AI systems in information security. (AU) | |
| Processo FAPESP: | 23/12830-0 - Sistemas Inteligentes Quânticos para Segurança Cibernética |
| Beneficiário: | Kelton Augusto Pontara da Costa |
| Modalidade de apoio: | Auxílio à Pesquisa - Regular |