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Detection and localization of splicing on remote sensing images using image-to-image transformation

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A Correction to this article was published on 28 February 2023

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Abstract

Remote sensing images can be easily tampered with user-friendly tools to hide important information. It necessitated the development of automatic splicing detection methods. The existing few methods concentrate on the semantic content in images for tamper detection and are not robust. On the contrary, we hypothesize that residual noise is independent of the semantic content and embeds the tampering traces; it is helpful for splice detection. In view of this, we focus on residual noise and formulate the problem as an image-to-image transformation, and model it using a U-net architecture. To suppress semantic content and extract the residual noise, we introduce a constrained convolutional layer in the U-net model. The model processes the input image and yields a map that localizes tampering in case of splicing. The model is trained using the conditional generative adversarial network (cGAN) framework. The loss function is composed of the cross-entropy, the Jaccard, and the end-point error (EPE) loss functions to enhance the detection and localization of tampered regions. To evaluate the proposed method, we develop a new dataset containing remote sensing images from different satellites and aerial sensors. The model detects splicing at pixel and image levels with high accuracy. It shows good robustness against well-known post-processing operations, including Gaussian blurring (GB) and white additive Gaussian noise (WAGN).

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Acknowledgements

The research was supported under Researchers Supporting Project number (RSP-2023/109) King Saud University, Riyadh, Saudi Arabia.

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Authors and Affiliations

  1. National Satellite Technology Center, King Abdulaziz City for Science and Technology, Riyadh, 11442, Saudi Arabia

    Rawan Alsughayer

  2. Department of Computer Science, CCIS, King Saud University, Riyadh, 11543, Saudi Arabia

    Muhammad Hussain, Fahman Saeed & Hatim AboalSamh

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  1. Rawan Alsughayer
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  2. Muhammad Hussain
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  3. Fahman Saeed
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  4. Hatim AboalSamh
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Correspondence to Muhammad Hussain.

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The original online version of this article was revised: The original version of this article contained a mistake in the acknowledgements. The correct acknowledgement should be "The research was supported under Researchers Supporting Project number (RSP-2023/109) King Saud University, Riyadh, Saudi Arabia" not "The research was supported under Researchers Supporting Project number (RSP-2019/109) King Saud University, Riyadh, Saudi Arabia."

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Alsughayer, R., Hussain, M., Saeed, F. et al. Detection and localization of splicing on remote sensing images using image-to-image transformation. Appl Intell 53, 13275–13292 (2023). https://doi.org/10.1007/s10489-022-04126-7

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  • DOI: https://doi.org/10.1007/s10489-022-04126-7

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