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IRL-Net: Inpainted Region Localization Network via Spatial Attention

Publicated to:IEEE Access. 11 115677-115687 - 2023-01-01 11(), DOI: 10.1109/ACCESS.2023.3324541

Authors: Daryani, AE; Mirmahdi, M; Hassanpour, A; Shahreza, HO; Yang, B; Fierrez, J

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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. We also provide the implementation of the proposed approach to support reproducible research via https://github.com/amiretefaghi/IRL-Net.

Keywords

convolutional neural networksdecodingforensicsimage forensicsimage inpaintingimage manipulation detectionimage reconstructionlocation awarenessstreaming mediatrainingConvolutional neural networksDecodingFeature extractionImage forensicsImage inpaintingImage manipulation detectionImage reconstructionLocation awarenessStreaming mediaTraining

Quality index

Bibliometric impact. Analysis of the contribution and dissemination channel

The work has been published in the journal IEEE Access due to its progression and the good impact it has achieved in recent years, according to the agency Scopus (SJR), it has become a reference in its field. In the year of publication of the work, 2023, it was in position , thus managing to position itself as a Q1 (Primer Cuartil), in the category Engineering (Miscellaneous).

From a relative perspective, and based on the normalized impact indicator calculated from the Field Citation Ratio (FCR) of the Dimensions source, it yields a value of: 1.23, which indicates that, compared to works in the same discipline and in the same year of publication, it ranks as a work cited above average. (source consulted: Dimensions Jun 2025)

Specifically, and according to different indexing agencies, this work has accumulated citations as of 2025-06-09, the following number of citations:

  • Scopus: 2

Impact and social visibility

From the perspective of influence or social adoption, and based on metrics associated with mentions and interactions provided by agencies specializing in calculating the so-called "Alternative or Social Metrics," we can highlight as of 2025-06-09:

  • The use of this contribution in bookmarks, code forks, additions to favorite lists for recurrent reading, as well as general views, indicates that someone is using the publication as a basis for their current work. This may be a notable indicator of future more formal and academic citations. This claim is supported by the result of the "Capture" indicator, which yields a total of: 8 (PlumX).

With a more dissemination-oriented intent and targeting more general audiences, we can observe other more global scores such as:

    It is essential to present evidence supporting full alignment with institutional principles and guidelines on Open Science and the Conservation and Dissemination of Intellectual Heritage. A clear example of this is:

    • The work has been submitted to a journal whose editorial policy allows open Open Access publication.
    • Assignment of a Handle/URN as an identifier within the deposit in the Institutional Repository: https://repositorio.uam.es/handle/10486/711829

    Leadership analysis of institutional authors

    This work has been carried out with international collaboration, specifically with researchers from: Iran; Norway; Switzerland.

    There is a significant leadership presence as some of the institution’s authors appear as the first or last signer, detailed as follows: Last Author (FIERREZ AGUILAR, JULIAN).