Journal of Jilin University(Engineering and Technology Edition) ›› 2021, Vol. 51 ›› Issue (5): 1838-1844.doi: 10.13229/j.cnki.jdxbgxb20210400

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Image manipulation localization algorithm based on channel attention convolutional neural networks

Hui ZHONG1(),Heng KANG2,Ying-da LYU3,Zhen-jian LI1,Hong LI1,Ruo-chuan OUYANG1   

  1. 1.Management Center of Big Data and Network，Jilin University，Changchun 130012，China
    2.ZICT Technology Co. ，Ltd. ，Shenzhen 518000，China
    3.Center for Computer Fundamental Education，Jilin University，Changchun 130012，China
  • Received:2021-05-06 Online:2021-09-01 Published:2021-09-16
  • Contact: Ying-da LYU E-mail:zhongh@jlu.edu.cn

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Abstract:

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.

Key words: channel attention, convolutional neural networks, image manipulation localization, feature extraction

CLC Number: 

  • TP391

Fig.1

Architecture of our CA-Net"

Fig.2

Structure of channel attention module with residual connection"

Table 1

Number of training and test images in image datasets"

SynthesizedNC2016CASIA
训练集65 0004045123 （CASIA v2.0）
测试集-160921 （CASIA v1.0）

Table 2

Results of choices of dilated convolutional layer"

孔洞卷积层准确率/%

采样步长｛1，3，5，7｝

采样步长｛2，4，6，8｝

常规卷积层

94.32
93.22
92.13

Table 3

Results of CA-Net with or without channel attention module"

通道注意力模块准确率/%
CA_090.15
CA_192.76
CA_293.17
CA_3（本文算法）94.32

Table 4

Pixel level F1 comparison on two datasets"

算法F1
NC2016CASIA v1.0
Error Level Analysis0.2360.214
Noise Inconsistencies0.2850.263
CFA0.1740.207
LSTM-EnDec-0.391
RGB-N0.7220.408
CA_3（本文算法）0.7950.425

Table 5

Pixel level AUC comparison on two datasets"

算法AUC/%
NC2016CASIA v1.0
Error Level Analysis42.961.3
Noise Inconsistencies48.761.2
CFA50.152.2
J-Conv-LSTM-Conv76.4-
LSTM-EnDec79.376.2
ManTra-Net79.581.7
CA_3（本文算法）83.181.9

Fig.3

Qualitative visualization for image manipulation detection on NC2016 and CASIA v1.0 datasets"

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