IFLNET: Image Forgery Localization Using Dual Attention Network

Abstract

The fake images and visuals can easily spread among social media users and they largely impact decisionmaking in society. Image forgery has become increasingly common as more non-professionals have access to image manipulation tools. These fake images are so sneaky that an ordinary person cannot guess them. Through social media, such photos are utilized to promote erroneous information in society. Image forgery detection is about segmenting the forged part from the images, primarily a region of interest. This paper suggests a unique method that depends on a dual attention network to detect forged segments. This network contains self-attention modules that contribute to extracting and matching features in the channel and spatial domains. These features help locate and identify the forged portions of digital images at various scales and channels. This experimental study uses typical datasets such as CASIA V1.0, CASIA V2.0, and Columbia. Proposed IFLNet technique outperforms other advanced techniques with a precision of 96 %, recall rate of 95 %, accuracy rate of 98 %, F1-score of 96 % and IoU score of 92 % for Columbia dataset and correspondingly other two datasets also