-
[pdf]
[supp]
[bibtex]@InProceedings{Qu_2024_CVPR, author = {Qu, Chenfan and Zhong, Yiwu and Liu, Chongyu and Xu, Guitao and Peng, Dezhi and Guo, Fengjun and Jin, Lianwen}, title = {Towards Modern Image Manipulation Localization: A Large-Scale Dataset and Novel Methods}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, month = {June}, year = {2024}, pages = {10781-10790} }
Towards Modern Image Manipulation Localization: A Large-Scale Dataset and Novel Methods
Abstract
In recent years image manipulation localization has attracted increasing attention due to its pivotal role in ensuring social media security. However effectively identifying forged regions remains an open challenge. The high acquisition cost and the severe scarcity of high-quality data are major factors hindering the performance improvement of modern image manipulation localization systems. To address this issue we propose a novel paradigm termed as CAAA to automatically and accurately annotate the manually forged images from the web at the pixel-level. We further propose a novel metric termed as QES to assist in filtering out unreliable annotations. With CAAA and QES we construct a large-scale diverse and high-quality dataset comprising 123150 manually forged images with mask annotations. Furthermore we develop a new model termed as APSC-Net for accurate image manipulation localization. According to extensive experiments our methods outperforms previous state-of-the-art methods our dataset significantly improves the performance of various models on the widely-used benchmarks. The dataset and codes are publicly available at https://github.com/qcf-568/MIML.
Related Material