Journal of Computer Applications ›› 2024, Vol. 44 ›› Issue (10): 3177-3184.DOI: 10.11772/j.issn.1001-9081.2023101462
• Multimedia computing and computer simulation • Previous Articles Next Articles
Pengbo WANG1, Wuyang SHAN1(), Jun LI1, Mao TIAN2, Deng ZOU1, Zhanfeng FAN3
Received:
2023-11-10
Revised:
2024-04-12
Accepted:
2024-04-15
Online:
2024-10-15
Published:
2024-10-10
Contact:
Wuyang SHAN
About author:
WANG Pengbo, born in 1998, M. S. candidate. His research interests include image forensics, deep learning.Supported by:
王朋博1, 单武扬1(), 李军1, 田茂2, 邹登1, 范占锋3
通讯作者:
单武扬
作者简介:
王朋博(1998—),男,河南漯河人,硕士研究生,CCF会员,主要研究方向:图像取证、深度学习基金资助:
CLC Number:
Pengbo WANG, Wuyang SHAN, Jun LI, Mao TIAN, Deng ZOU, Zhanfeng FAN. Robust splicing forensic algorithm against high-intensity salt-and-pepper noise[J]. Journal of Computer Applications, 2024, 44(10): 3177-3184.
王朋博, 单武扬, 李军, 田茂, 邹登, 范占锋. 抗高强度椒盐噪声的鲁棒拼接取证算法[J]. 《计算机应用》唯一官方网站, 2024, 44(10): 3177-3184.
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URL: https://www.joca.cn/EN/10.11772/j.issn.1001-9081.2023101462
数据集 | 样本数 | 图像分辨率 | 图像格式 |
---|---|---|---|
DSO-1(RGB) | 500 | 2 048×1 536~1 536×2 048 | PNG |
DSO-1(灰度) | 500 | 2 048×1 536~1 536×2 048 | PNG |
Columbia(RGB) | 900 | 757×568~1 152×768 | PNG |
Columbia(灰度) | 900 | 757×568~1 152×768 | PNG |
Tab. 1 Information of datasets
数据集 | 样本数 | 图像分辨率 | 图像格式 |
---|---|---|---|
DSO-1(RGB) | 500 | 2 048×1 536~1 536×2 048 | PNG |
DSO-1(灰度) | 500 | 2 048×1 536~1 536×2 048 | PNG |
Columbia(RGB) | 900 | 757×568~1 152×768 | PNG |
Columbia(灰度) | 900 | 757×568~1 152×768 | PNG |
噪声强度/% | 仅取证 | 本文算法 | ||
---|---|---|---|---|
MCC | F1 | MCC | F1 | |
1 | 0.631 | 0.961 | 0.645 | 0.962 |
5 | 0.139 | 0.923 | 0.514 | 0.947 |
10 | 0.132 | 0.949 | 0.474 | 0.944 |
30 | 0.166 | 0.922 | 0.377 | 0.936 |
50 | 0.165 | 0.922 | 0.303 | 0.932 |
Tab. 2 Results of ablation experiments (RGB images in DSO-1 dataset)
噪声强度/% | 仅取证 | 本文算法 | ||
---|---|---|---|---|
MCC | F1 | MCC | F1 | |
1 | 0.631 | 0.961 | 0.645 | 0.962 |
5 | 0.139 | 0.923 | 0.514 | 0.947 |
10 | 0.132 | 0.949 | 0.474 | 0.944 |
30 | 0.166 | 0.922 | 0.377 | 0.936 |
50 | 0.165 | 0.922 | 0.303 | 0.932 |
噪声强度/% | 仅取证 | 本文算法 | ||
---|---|---|---|---|
MCC | F1 | MCC | F1 | |
1 | 0.312 | 0.927 | 0.449 | 0.941 |
5 | 0.151 | 0.923 | 0.417 | 0.938 |
10 | 0.133 | 0.922 | 0.402 | 0.937 |
30 | 0.165 | 0.922 | 0.345 | 0.933 |
50 | 0.173 | 0.922 | 0.266 | 0.929 |
Tab. 3 Results of ablation experiments (grayscale images in DSO-1 dataset)
噪声强度/% | 仅取证 | 本文算法 | ||
---|---|---|---|---|
MCC | F1 | MCC | F1 | |
1 | 0.312 | 0.927 | 0.449 | 0.941 |
5 | 0.151 | 0.923 | 0.417 | 0.938 |
10 | 0.133 | 0.922 | 0.402 | 0.937 |
30 | 0.165 | 0.922 | 0.345 | 0.933 |
50 | 0.173 | 0.922 | 0.266 | 0.929 |
噪声强度/% | 仅取证 | 本文算法 | ||
---|---|---|---|---|
MCC | F1 | MCC | F1 | |
1 | 0.556 | 0.673 | 0.736 | 0.800 |
5 | 0.324 | 0.499 | 0.660 | 0.741 |
10 | 0.257 | 0.465 | 0.635 | 0.722 |
30 | 0.282 | 0.481 | 0.563 | 0.672 |
50 | 0.284 | 0.444 | 0.459 | 0.597 |
Tab. 4 Results of ablation experiments (RGB images in Columbia dataset )
噪声强度/% | 仅取证 | 本文算法 | ||
---|---|---|---|---|
MCC | F1 | MCC | F1 | |
1 | 0.556 | 0.673 | 0.736 | 0.800 |
5 | 0.324 | 0.499 | 0.660 | 0.741 |
10 | 0.257 | 0.465 | 0.635 | 0.722 |
30 | 0.282 | 0.481 | 0.563 | 0.672 |
50 | 0.284 | 0.444 | 0.459 | 0.597 |
噪声强度/% | 仅取证 | 本文算法 | ||
---|---|---|---|---|
MCC | F1 | MCC | F1 | |
1 | 0.556 | 0.674 | 0.709 | 0.782 |
5 | 0.305 | 0.495 | 0.661 | 0.742 |
10 | 0.271 | 0.467 | 0.609 | 0.702 |
30 | 0.281 | 0.476 | 0.550 | 0.657 |
50 | 0.287 | 0.450 | 0.450 | 0.589 |
Tab. 5 Results of ablation experiments (grayscale images in Columbia dataset)
噪声强度/% | 仅取证 | 本文算法 | ||
---|---|---|---|---|
MCC | F1 | MCC | F1 | |
1 | 0.556 | 0.674 | 0.709 | 0.782 |
5 | 0.305 | 0.495 | 0.661 | 0.742 |
10 | 0.271 | 0.467 | 0.609 | 0.702 |
30 | 0.281 | 0.476 | 0.550 | 0.657 |
50 | 0.287 | 0.450 | 0.450 | 0.589 |
噪声 强度/% | EVP[ | CAGI[ | PSCC-Net[ | FS[ | NOI2[ | ADQ2[ | 本文算法 | |||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
MCC | F1 | MCC | F1 | MCC | F1 | MCC | F1 | MCC | F1 | MCC | F1 | MCC | F1 | |
1 | 0.100 | 0.918 | 0.187 | 0.923 | 0.306 | 0.929 | 0.312 | 0.922 | 0.113 | 0.922 | 0.006 | 0.004 | 0.645 | 0.962 |
5 | 0.160 | 0.918 | 0.138 | 0.922 | 0.339 | 0.925 | 0.204 | 0.922 | 0.141 | 0.923 | 0.003 | 0.004 | 0.514 | 0.947 |
10 | 0.199 | 0.918 | 0.144 | 0.922 | 0.298 | 0.922 | 0.213 | 0.922 | 0.200 | 0.922 | 0.004 | 0.004 | 0.474 | 0.944 |
Tab. 6 Performance comparison of different forensic algorithms on DSO-1 noise dataset
噪声 强度/% | EVP[ | CAGI[ | PSCC-Net[ | FS[ | NOI2[ | ADQ2[ | 本文算法 | |||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
MCC | F1 | MCC | F1 | MCC | F1 | MCC | F1 | MCC | F1 | MCC | F1 | MCC | F1 | |
1 | 0.100 | 0.918 | 0.187 | 0.923 | 0.306 | 0.929 | 0.312 | 0.922 | 0.113 | 0.922 | 0.006 | 0.004 | 0.645 | 0.962 |
5 | 0.160 | 0.918 | 0.138 | 0.922 | 0.339 | 0.925 | 0.204 | 0.922 | 0.141 | 0.923 | 0.003 | 0.004 | 0.514 | 0.947 |
10 | 0.199 | 0.918 | 0.144 | 0.922 | 0.298 | 0.922 | 0.213 | 0.922 | 0.200 | 0.922 | 0.004 | 0.004 | 0.474 | 0.944 |
前提 条件 | 噪声 强度/% | CAGI[ | FS[ | CAT-Net[ | PSCC-Net[ | ||||
---|---|---|---|---|---|---|---|---|---|
MCC | F1 | MCC | F1 | MCC | F1 | MCC | F1 | ||
无预 处理 | 1 | 0.187 | 0.923 | 0.312 | 0.922 | 0.288 | 0.925 | 0.306 | 0.929 |
5 | 0.138 | 0.922 | 0.204 | 0.922 | 0.241 | 0.923 | 0.339 | 0.925 | |
10 | 0.144 | 0.922 | 0.213 | 0.922 | 0.225 | 0.922 | 0.298 | 0.922 | |
有预 处理 | 1 | 0.404 | 0.936 | 0.484 | 0.925 | 0.452 | 0.937 | 0.568 | 0.952 |
5 | 0.377 | 0.936 | 0.444 | 0.924 | 0.404 | 0.934 | 0.482 | 0.944 | |
10 | 0.397 | 0.936 | 0.454 | 0.924 | 0.389 | 0.933 | 0.443 | 0.940 |
Tab. 7 Comparison of universal effects of preprocessing on DSO-1 dataset
前提 条件 | 噪声 强度/% | CAGI[ | FS[ | CAT-Net[ | PSCC-Net[ | ||||
---|---|---|---|---|---|---|---|---|---|
MCC | F1 | MCC | F1 | MCC | F1 | MCC | F1 | ||
无预 处理 | 1 | 0.187 | 0.923 | 0.312 | 0.922 | 0.288 | 0.925 | 0.306 | 0.929 |
5 | 0.138 | 0.922 | 0.204 | 0.922 | 0.241 | 0.923 | 0.339 | 0.925 | |
10 | 0.144 | 0.922 | 0.213 | 0.922 | 0.225 | 0.922 | 0.298 | 0.922 | |
有预 处理 | 1 | 0.404 | 0.936 | 0.484 | 0.925 | 0.452 | 0.937 | 0.568 | 0.952 |
5 | 0.377 | 0.936 | 0.444 | 0.924 | 0.404 | 0.934 | 0.482 | 0.944 | |
10 | 0.397 | 0.936 | 0.454 | 0.924 | 0.389 | 0.933 | 0.443 | 0.940 |
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