A Noise Convolution Network for Tampering Detection

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

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


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 languageEnglish
Title of host publicationArtificial Neural Networks and Machine Learning – ICANN 2023 - 32nd International Conference on Artificial Neural Networks, Proceedings
EditorsLazaros Iliadis, Antonios Papaleonidas, Plamen Angelov, Chrisina Jayne
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages11
ISBN (Print)9783031442032
Publication statusPublished - 2023
Event32nd International Conference on Artificial Neural Networks, ICANN 2023 - Heraklion, Greece
Duration: 26 Sept 202329 Sept 2023

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume14263 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference32nd International Conference on Artificial Neural Networks, ICANN 2023


  • Deep Learning
  • Neural Network
  • Noise enhancement
  • Tampering Detection


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