A Noise Convolution Network for Tampering Detection

Zhiyao Xie; Xiaochen Yuan; Chan Tong Lam; Guoheng Huang

doi:10.1007/978-3-031-44204-9_4

A Noise Convolution Network for Tampering Detection

Zhiyao Xie, Xiaochen Yuan, Chan Tong Lam, Guoheng Huang

Faculty of Applied Sciences

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

The vulnerability of digital images to tampering is an ongoing information security issue in the multimedia field. Thus, identifying tampered digital images and locating the tampered regions in the images can help improve the security of information dissemination. A deep fusion neural network named NC-Net is designed in this paper, introducing pattern noise as assistance to fully exploit the tampered features present on the tampered image. The incorporation of noise texture information enabled NC-Net to acquire deeper tampered image features during the training phase. The extracted noise is incorporated as a crucial component within the convolutional structure of the model, serving as a potent activation signal for the tampered region. The performance of NC-Net is confirmed through relevant experiments on publicly available tampered datasets, and outstanding results are achieved in comparison to other methods.

Original language	English
Title of host publication	Artificial Neural Networks and Machine Learning – ICANN 2023 - 32nd International Conference on Artificial Neural Networks, Proceedings
Editors	Lazaros Iliadis, Antonios Papaleonidas, Plamen Angelov, Chrisina Jayne
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	38-48
Number of pages	11
ISBN (Print)	9783031442032
DOIs	https://doi.org/10.1007/978-3-031-44204-9_4
Publication status	Published - 2023
Event	32nd International Conference on Artificial Neural Networks, ICANN 2023 - Heraklion, Greece Duration: 26 Sept 2023 → 29 Sept 2023

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	14263 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	32nd International Conference on Artificial Neural Networks, ICANN 2023
Country/Territory	Greece
City	Heraklion
Period	26/09/23 → 29/09/23

Keywords

Deep Learning
Neural Network
Noise enhancement
Tampering Detection

Access to Document

10.1007/978-3-031-44204-9_4

Cite this

Xie, Z., Yuan, X., Lam, C. T., & Huang, G. (2023). A Noise Convolution Network for Tampering Detection. In L. Iliadis, A. Papaleonidas, P. Angelov, & C. Jayne (Eds.), Artificial Neural Networks and Machine Learning – ICANN 2023 - 32nd International Conference on Artificial Neural Networks, Proceedings (pp. 38-48). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 14263 LNCS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-44204-9_4

Xie, Zhiyao ; Yuan, Xiaochen ; Lam, Chan Tong et al. / A Noise Convolution Network for Tampering Detection. Artificial Neural Networks and Machine Learning – ICANN 2023 - 32nd International Conference on Artificial Neural Networks, Proceedings. editor / Lazaros Iliadis ; Antonios Papaleonidas ; Plamen Angelov ; Chrisina Jayne. Springer Science and Business Media Deutschland GmbH, 2023. pp. 38-48 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "A Noise Convolution Network for Tampering Detection",

abstract = "The vulnerability of digital images to tampering is an ongoing information security issue in the multimedia field. Thus, identifying tampered digital images and locating the tampered regions in the images can help improve the security of information dissemination. A deep fusion neural network named NC-Net is designed in this paper, introducing pattern noise as assistance to fully exploit the tampered features present on the tampered image. The incorporation of noise texture information enabled NC-Net to acquire deeper tampered image features during the training phase. The extracted noise is incorporated as a crucial component within the convolutional structure of the model, serving as a potent activation signal for the tampered region. The performance of NC-Net is confirmed through relevant experiments on publicly available tampered datasets, and outstanding results are achieved in comparison to other methods.",

keywords = "Deep Learning, Neural Network, Noise enhancement, Tampering Detection",

author = "Zhiyao Xie and Xiaochen Yuan and Lam, {Chan Tong} and Guoheng Huang",

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Xie, Z, Yuan, X , Lam, CT & Huang, G 2023, A Noise Convolution Network for Tampering Detection. in L Iliadis, A Papaleonidas, P Angelov & C Jayne (eds), Artificial Neural Networks and Machine Learning – ICANN 2023 - 32nd International Conference on Artificial Neural Networks, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 14263 LNCS, Springer Science and Business Media Deutschland GmbH, pp. 38-48, 32nd International Conference on Artificial Neural Networks, ICANN 2023, Heraklion, Greece, 26/09/23. https://doi.org/10.1007/978-3-031-44204-9_4

A Noise Convolution Network for Tampering Detection. / Xie, Zhiyao; Yuan, Xiaochen ; Lam, Chan Tong et al.
Artificial Neural Networks and Machine Learning – ICANN 2023 - 32nd International Conference on Artificial Neural Networks, Proceedings. ed. / Lazaros Iliadis; Antonios Papaleonidas; Plamen Angelov; Chrisina Jayne. Springer Science and Business Media Deutschland GmbH, 2023. p. 38-48 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 14263 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AB - The vulnerability of digital images to tampering is an ongoing information security issue in the multimedia field. Thus, identifying tampered digital images and locating the tampered regions in the images can help improve the security of information dissemination. A deep fusion neural network named NC-Net is designed in this paper, introducing pattern noise as assistance to fully exploit the tampered features present on the tampered image. The incorporation of noise texture information enabled NC-Net to acquire deeper tampered image features during the training phase. The extracted noise is incorporated as a crucial component within the convolutional structure of the model, serving as a potent activation signal for the tampered region. The performance of NC-Net is confirmed through relevant experiments on publicly available tampered datasets, and outstanding results are achieved in comparison to other methods.

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Xie Z, Yuan X , Lam CT, Huang G. A Noise Convolution Network for Tampering Detection. In Iliadis L, Papaleonidas A, Angelov P, Jayne C, editors, Artificial Neural Networks and Machine Learning – ICANN 2023 - 32nd International Conference on Artificial Neural Networks, Proceedings. Springer Science and Business Media Deutschland GmbH. 2023. p. 38-48. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-031-44204-9_4