Self-Adversarial Training Incorporating Forgery Attention for Image Forgery Localization
نویسندگان
چکیده
Image editing techniques enable people to modify the content of an image without leaving visual traces and thus may cause serious security risks. Hence detection localization these forgeries become quite necessary challenging. Furthermore, unlike other tasks with extensive data, there is usually a lack annotated forged images for training due annotation difficulties. In this paper, we propose self-adversarial strategy reliable coarse-to-fine network that utilizes self-attention mechanism localize regions in forgery images. The module based on Channel-Wise High Pass Filter block (CW-HPF). CW-HPF leverages inter-channel relationships features extracts noise by high pass filters. Based CW-HPF, mechanism, called attention, proposed capture rich contextual dependencies intrinsic inconsistency extracted from tampered regions. Specifically, append two types attention modules top respectively model internal interdependencies spatial dimension external among channels. We exploit enhance between original More importantly, address issue insufficient design expands data dynamically achieve more robust performance. each iteration, perform adversarial attacks against our generate examples train them. Extensive experimental results demonstrate algorithm steadily outperforms state-of-the-art methods clear margin different benchmark datasets.
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ژورنال
عنوان ژورنال: IEEE Transactions on Information Forensics and Security
سال: 2022
ISSN: ['1556-6013', '1556-6021']
DOI: https://doi.org/10.1109/tifs.2022.3152362