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AI-Generated Face Image Identification with Different Color Space Channel Combinations

With the rapid development of the Internet and information technology (in particular, generative adversarial networks and deep learning), network data are exploding. Due to the misuse of technology and inadequate supervision, deep-network-generated face images flood the network, and the forged image...

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Detalles Bibliográficos
Autores principales: Mo, Songwen, Lu, Pei, Liu, Xiaoyong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9659029/
https://www.ncbi.nlm.nih.gov/pubmed/36365924
http://dx.doi.org/10.3390/s22218228
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author Mo, Songwen
Lu, Pei
Liu, Xiaoyong
author_facet Mo, Songwen
Lu, Pei
Liu, Xiaoyong
author_sort Mo, Songwen
collection PubMed
description With the rapid development of the Internet and information technology (in particular, generative adversarial networks and deep learning), network data are exploding. Due to the misuse of technology and inadequate supervision, deep-network-generated face images flood the network, and the forged image is called a deepfake. Those realistic faked images launched a serious challenge to the human eye and the automatic identification system, resulting in many legal, ethical, and social issues. For the needs of network information security, deep-network-generated face image identification based on different color spaces is proposed. Due to the extremely realistic effect of deepfake images, it is difficult to achieve high accuracy with ordinary methods for neural networks, so we used the image processing method here. First, by analyzing the differences in different color space components in the deep learning network model for face sensitivity, a combination of color space components that can effectively improve the discrimination rate of the deep learning network model is given. Second, to further improve the discriminative performance of the model, a channel attention mechanism was added at the shallow level of the model to further focus on the features contributing to the model. The experimental results show that this scheme achieved better accuracy in the same face generation model and in different face generation models than the two compared methods, and its accuracy reached up to 99.10% in the same face generation model. Meanwhile, the accuracy of this model only decreased to 98.71% when coping with a JPEG compression factor of 100, which shows that this model is robust.
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spelling pubmed-96590292022-11-15 AI-Generated Face Image Identification with Different Color Space Channel Combinations Mo, Songwen Lu, Pei Liu, Xiaoyong Sensors (Basel) Article With the rapid development of the Internet and information technology (in particular, generative adversarial networks and deep learning), network data are exploding. Due to the misuse of technology and inadequate supervision, deep-network-generated face images flood the network, and the forged image is called a deepfake. Those realistic faked images launched a serious challenge to the human eye and the automatic identification system, resulting in many legal, ethical, and social issues. For the needs of network information security, deep-network-generated face image identification based on different color spaces is proposed. Due to the extremely realistic effect of deepfake images, it is difficult to achieve high accuracy with ordinary methods for neural networks, so we used the image processing method here. First, by analyzing the differences in different color space components in the deep learning network model for face sensitivity, a combination of color space components that can effectively improve the discrimination rate of the deep learning network model is given. Second, to further improve the discriminative performance of the model, a channel attention mechanism was added at the shallow level of the model to further focus on the features contributing to the model. The experimental results show that this scheme achieved better accuracy in the same face generation model and in different face generation models than the two compared methods, and its accuracy reached up to 99.10% in the same face generation model. Meanwhile, the accuracy of this model only decreased to 98.71% when coping with a JPEG compression factor of 100, which shows that this model is robust. MDPI 2022-10-27 /pmc/articles/PMC9659029/ /pubmed/36365924 http://dx.doi.org/10.3390/s22218228 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Mo, Songwen
Lu, Pei
Liu, Xiaoyong
AI-Generated Face Image Identification with Different Color Space Channel Combinations
title AI-Generated Face Image Identification with Different Color Space Channel Combinations
title_full AI-Generated Face Image Identification with Different Color Space Channel Combinations
title_fullStr AI-Generated Face Image Identification with Different Color Space Channel Combinations
title_full_unstemmed AI-Generated Face Image Identification with Different Color Space Channel Combinations
title_short AI-Generated Face Image Identification with Different Color Space Channel Combinations
title_sort ai-generated face image identification with different color space channel combinations
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9659029/
https://www.ncbi.nlm.nih.gov/pubmed/36365924
http://dx.doi.org/10.3390/s22218228
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