{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,8]],"date-time":"2026-07-08T16:07:57Z","timestamp":1783526877889,"version":"3.55.0"},"reference-count":19,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2025,11,18]],"date-time":"2025-11-18T00:00:00Z","timestamp":1763424000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001807","name":"S\u00e3o Paulo Research Foundation","doi-asserted-by":"crossref","award":["2023\/12830-0"],"award-info":[{"award-number":["2023\/12830-0"]}],"id":[{"id":"10.13039\/501100001807","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Imaging"],"abstract":"<jats:p>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\u2019s 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\u2019s 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\u2019s 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.<\/jats:p>","DOI":"10.3390\/jimaging11110416","type":"journal-article","created":{"date-parts":[[2025,11,18]],"date-time":"2025-11-18T12:33:04Z","timestamp":1763469184000},"page":"416","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Seam Carving Forgery Detection Through Multi-Perspective Explainable AI"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-7065-3668","authenticated-orcid":false,"given":"Miguel Jos\u00e9 das","family":"Neves","sequence":"first","affiliation":[{"name":"School of Sciences, S\u00e3o Paulo State University (UNESP), Bauru 17033-360, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9816-1440","authenticated-orcid":false,"given":"Felipe Rodrigues Perche","family":"Mahlow","sequence":"additional","affiliation":[{"name":"School of Sciences, S\u00e3o Paulo State University (UNESP), Bauru 17033-360, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9353-8317","authenticated-orcid":false,"given":"Renato Dias de","family":"Souza","sequence":"additional","affiliation":[{"name":"School of Sciences, S\u00e3o Paulo State University (UNESP), Bauru 17033-360, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3970-7818","authenticated-orcid":false,"suffix":"Jr.","given":"Paulo Roberto G.","family":"Hernandes","sequence":"additional","affiliation":[{"name":"School of Sciences, S\u00e3o Paulo State University (UNESP), Bauru 17033-360, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jos\u00e9 Remo Ferreira","family":"Brega","sequence":"additional","affiliation":[{"name":"School of Sciences, S\u00e3o Paulo State University (UNESP), Bauru 17033-360, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5458-3908","authenticated-orcid":false,"given":"Kelton Augusto Pontara da","family":"Costa","sequence":"additional","affiliation":[{"name":"School of Sciences, S\u00e3o Paulo State University (UNESP), Bauru 17033-360, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,11,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"52138","DOI":"10.1109\/ACCESS.2018.2870052","article-title":"Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI)","volume":"6","author":"Adadi","year":"2018","journal-title":"IEEE Access"},{"key":"ref_2","unstructured":"Doshi-Velez, F., and Kim, B. (2017). Towards A Rigorous Science of Interpretable Machine Learning. arXiv."},{"key":"ref_3","first-page":"1","article-title":"Visualizing higher-layer features of a deep network","volume":"1341","author":"Erhan","year":"2009","journal-title":"Univ. Montr."},{"key":"ref_4","unstructured":"Lundberg, S.M., and Lee, S.I. (2017, January 4\u20139). A Unified Approach to Interpreting Model Predictions. Proceedings of the Advances in Neural Information Processing Systems 30 (NIPS 2017), Long Beach, CA, USA."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., and Batra, D. (2017, January 22\u201329). Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization. Proceedings of the 2017 IEEE International Conference on Computer Vision (ICCV), Venice, Italy.","DOI":"10.1109\/ICCV.2017.74"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Avidan, S., and Shamir, A. (2007). Seam carving for content-aware image resizing. ACM Trans. Graph. (TOG), 26.","DOI":"10.1145\/1239451.1239461"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Dietterich, T.G. (2000). Ensemble Methods in Machine Learning. Multiple Classifier Systems, Springer.","DOI":"10.1007\/3-540-45014-9_1"},{"key":"ref_8","first-page":"1","article-title":"Improved Approaches with Calibrated Neighboring Joint Density to Steganalysis and Seam-Carved Forgery Detection in JPEG Images","volume":"5","author":"Liu","year":"2015","journal-title":"ACM Trans. Intell. Syst. Technol. (TIST)"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Nam, S.H., Ahn, W., Mun, S.M., Park, J., Kim, D., Yu, I.J., and Lee, H.K. (2019, January 22\u201325). Content-Aware Image Resizing Detection Using Deep Neural Network. Proceedings of the 2019 IEEE International Conference on Image Processing (ICIP), Taipei, Taiwan.","DOI":"10.1109\/ICIP.2019.8802946"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Ye, J., Shi, Y.Q., Xu, G., and Shi, Y.Q. (2019, January 18\u201319). A Convolutional Neural Network Based Seam Carving Detection Scheme for Uncompressed Digital Images. Proceedings of the Digital Forensics and Watermarking. Springer International Publishing, Guilin, China.","DOI":"10.1007\/978-3-030-11389-6_1"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1907","DOI":"10.1109\/TCSVT.2018.2859633","article-title":"An Improved Approach to Exposing JPEG Seam Carving Under Recompression","volume":"29","author":"Liu","year":"2019","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"88","DOI":"10.20517\/jsss.2022.02","article-title":"A comparison study to detect seam carving forgery in JPEG images with deep learning models","volume":"3","author":"Celebi","year":"2022","journal-title":"J. Surveill. Secur. Saf."},{"key":"ref_13","unstructured":"Molnar, C. (2025, January 15). Interpretable Machine Learning; Lulu.com. Available online: https:\/\/christophm.github.io\/interpretable-ml-book\/."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"22611","DOI":"10.1007\/s11042-020-10158-3","article-title":"Encoder-decoder based convolutional neural networks for image forgery detection","volume":"81","author":"Biach","year":"2021","journal-title":"Multimed. Tools Appl."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"379","DOI":"10.26599\/TST.2018.9010119","article-title":"A Classifier Using Online Bagging Ensemble Method for Big Data Stream Learning","volume":"24","author":"Lv","year":"2019","journal-title":"Tsinghua Sci. Technol."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"ref_17","unstructured":"Nair, V., and Hinton, G.E. (2010, January 21\u201324). Rectified Linear Units Improve Restricted Boltzmann Machines. Proceedings of the 27th International Conference on Machine Learning (ICML\u201910), Madison, WI, USA. Available online: https:\/\/www.researchgate.net\/publication\/221345737_Rectified_Linear_Units_Improve_Restricted_Boltzmann_Machines_Vinod_Nair."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Joseph, F.J.J., Nonsiri, S., and Monsakul, A. (2021). Keras and TensorFlow: A Hands-On Experience. Advanced Deep Learning for Engineers and Scientists: A Practical Approach, Springer.","DOI":"10.1007\/978-3-030-66519-7_4"},{"key":"ref_19","unstructured":"Simonyan, K., and Zisserman, A. (2015, January 7\u20139). Very Deep Convolutional Networks for Large-Scale Image Recognition. Proceedings of the International Conference on Learning Representations, San Diego, CA, USA."}],"container-title":["Journal of Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2313-433X\/11\/11\/416\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,20]],"date-time":"2025-11-20T05:26:20Z","timestamp":1763616380000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2313-433X\/11\/11\/416"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,18]]},"references-count":19,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2025,11]]}},"alternative-id":["jimaging11110416"],"URL":"https:\/\/doi.org\/10.3390\/jimaging11110416","relation":{},"ISSN":["2313-433X"],"issn-type":[{"value":"2313-433X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,11,18]]}}}