IRL-Net: Inpainted region localization network via spatial attention

Abstract

Identifying manipulated regions in images is a challenging task due to the existence of very accurate image inpainting techniques leaving almost unnoticeable traces in tampered regions. These image inpainting methods can be used for multiple purposes (e.g., removing objects, reconstructing corrupted areas, eliminating various types of distortion, etc.) makes creating forensic detectors for image manipulation an extremely difficult and time-consuming procedure. The aim of this paper is to localize the tampered regions manipulated by image inpainting methods. To do this, we propose a novel CNN-based deep learning model called IRL-Net which includes three main modules: Enhancement, Encoder, and Decoder modules. To evaluate our method, three image inpainting methods have been used to reconstruct the missed regions in two face and scene image datasets. We perform both qualitative and quantitative evaluations on the generated datasets. Experimental results demonstrate that our method outperforms previous learning-based manipulated region detection methods and generates realistic and semantically plausible images.

Output Images

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Bibtex

          @ARTICLE{10285582,
            author={Daryani, Amir Etefaghi and Mirmahdi, Mahdieh and Hassanpour, Ahmad and Shahreza, Hatef Otroshi and Yang, Bian and Fierrez, Julian},
            journal={IEEE Access}, 
            title={IRL-Net: Inpainted Region Localization Network via Spatial Attention}, 
            year={2023},
            volume={11},
            number={},
            pages={115677-115687},
            keywords={Feature extraction;Location awareness;Decoding;Training;Streaming media;Image reconstruction;Convolutional neural networks;Forensics;Image forensics;image inpainting;image manipulation detection},
            doi={10.1109/ACCESS.2023.3324541}}