Document Type
Conference Proceeding
Publication Date
9-27-2024
Publication Title
Proceedings of the 2024 IEEE International Conference on Image Processing (ICIP)
Pages
3257-3263
Publisher Name
IEEE
Abstract
The social media-fuelled explosion of fake news and misinformation supported by tampered images has led to growth in the development of models and datasets for image manipulation detection. However, existing detection methods mostly treat media objects in isolation, without considering the impact of specific manipulations on viewer perception. Forensic datasets are usually analyzed based on the manipulation operations and corresponding pixel-based masks, but not on the semantics of the manipulation, i.e., type of scene, objects, and viewers’ attention to scene content. The semantics of the manipulation play an important role in spreading misinformation through manipulated images. In an attempt to encourage further development of semantic-aware forensic approaches to understand visual misinformation, we propose a framework to analyze the trends of visual and semantic saliency in popular image manipulation datasets and their impact on detection. https://github. com/CV-Lehigh/Bias_IMD
Recommended Citation
@inproceedings{krinsky2024exploring, title={Exploring Saliency Bias in Manipulation Detection}, author={Krinsky, Joshua and Bettis, Alan and Tang, Qiuyu and Moreira, Daniel and Bharati, Aparna }, booktitle={IEEE International Conference on Image Processing}, pages={3257--3263}, year={2024} }
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 License.
Copyright Statement
© 2024, IEEE.
Comments
Author Posting © 2024, IEEE. This is the author's version of the work. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The definitive version of this work has been published at - https://doi.org/10.1109/ICIP51287.2024.10648063