Image manipulation localization algorithm based on channel attention convolutional neural networks

被引：0

作者：

Zhong H. ^{[1
]}

Kang H. ^{[2
]}

Lyu Y.-D. ^{[3
]}

Li Z.-J. ^{[1
]}

Li H. ^{[1
]}

Ouyang R.-C. ^{[1
]}

机构：

[1] Management Center of Big Data and Network, Jilin University, Changchun

[2] ZICT Technology Co., Ltd., Shenzhen

[3] Center for Computer Fundamental Education, Jilin University, Changchun

来源：

Jilin Daxue Xuebao (Gongxueban)/Journal of Jilin University (Engineering and Technology Edition) | 2021年 / 51卷 / 05期

关键词：

Channel attention; Convolutional neural networks; Feature extraction; Image manipulation localization;

D O I：

10.13229/j.cnki.jdxbgxb20210400

中图分类号：

学科分类号：

摘要：

To prevent manipulation of image content (such as splicing), this paper proposes an image manipulation localization algorithm based on Channel Attention Convolutional Neural Network, and it is called CA-Net. Although the powerful feature learning and mapping capabilities of CNNs can sequentially acquire rich spatial features, this paper proposes to use parallel dilated convolutional layers with different sampling steps to extract multi-scale features. At the same time, in order to make better use of the characteristic channel information, we additionally introduce a channel attention module in the decoding network. This experiment uses Synthesized image dataset for training, and fine-tunes and tests NC2016 and CASIA on the two image libraries. Experimental results show that the proposed parallel dilated convolutional layer and channel attention module can significantly improve the results. Compared with some of the state-of-the-art algorithms, CA-Net performs best on the two standard image datasets. © 2021, Jilin University Press. All right reserved.

引用

下载

页码：1838 / 1844

页数：6

共 24 条

[1] ZhaoHong -wei, LiuXiao -han, ZhangYuan, Et al., Clothing classification algorithm based on landmark attention and channel attention, Journal of Jilin University(Engineering and Technology Edition), 50, 5, pp. 1765-1770, (2020)
[2] Zhuang P, Li H, Tan S, Et al., Image tampering localization using a dense fully convolutional network, IEEE Transactions on Information Forensics and Security, 16, pp. 2986-2999, (2021)
[3] Ng T T, Chang S F, Sun Q., Blind detection of photomontage using higher order statistics, International Symposium on Circuits and Systems, (2004)
[4] Verdoliva L., Media forensics and deepfakes: an overview, IEEE Journal of Selected Topics in Signal Processing, 14, 5, pp. 910-932, (2020)
[5] Gaborini L, Bestagini P, Milani S, Et al., Multi-clue image tampering localization, IEEE International Workshop on Information Forensics and Security, pp. 125-130, (2014)
[6] Ferrara P, Bianchi T, Rosa A D, Et al., Image forgery localization via fine-grained analysis of CFA artifacts, IEEE Transactions on Information Forensics and Security, 7, 5, pp. 1566-1577, (2012)
[7] Amerini I, Uricchio T, Ballan L, Et al., Localization of JPEG double compression through multi-domain convolutional neural networks, IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 1865-1871, (2017)
[8] Salloum R, Ren Y, Kuo C-C J., Image splicing localization using a multi-task fully convolutional network, Journal of Visual Communication Image Representation, 51, pp. 201-209, (2018)
[9] Bappy J H, Simons C, Nataraj L, Et al., Hybrid LSTM and Encoder-Decoder architecture for detection of image forgeries, IEEE Transactions on Image Processing, 28, 7, pp. 3286-3300, (2019)
[10] ChenMian -shu, YuLu -lu, SuYue, Et al., Multi-label images classification based on convolutional neural network, Journal of Jilin University(Engineering and Technology Edition), 50, 3, pp. 1077-1084, (2020)

← 1 2 3 →