Journal of Computer Applications ›› 2024, Vol. 44 ›› Issue (4): 1303-1309.DOI: 10.11772/j.issn.1001-9081.2023040493
Special Issue: 多媒体计算与计算机仿真
• Multimedia computing and computer simulation • Previous Articles Next Articles
Shunwang FU1, Qian CHEN1(), Zhi LI2, Guomei WANG2, Yu LU3
Received:
2023-04-28
Revised:
2023-07-26
Accepted:
2023-07-31
Online:
2023-12-04
Published:
2024-04-10
Contact:
Qian CHEN
About author:
FU Shunwang, born in 1996, M.S. candidate. His research interests include deep learning, image tamper detection.Supported by:
通讯作者:
陈茜
作者简介:
付顺旺(1996—),男,贵州遵义人,硕士研究生,CCF会员,主要研究方向:深度学习、图像篡改检测基金资助:
CLC Number:
Shunwang FU, Qian CHEN, Zhi LI, Guomei WANG, Yu LU. Two-channel progressive feature filtering network for tampered image detection and localization[J]. Journal of Computer Applications, 2024, 44(4): 1303-1309.
付顺旺, 陈茜, 李智, 王国美, 卢妤. 用于篡改图像检测和定位的双通道渐进式特征过滤网络[J]. 《计算机应用》唯一官方网站, 2024, 44(4): 1303-1309.
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URL: https://www.joca.cn/EN/10.11772/j.issn.1001-9081.2023040493
数据集 | 图像数 | 复制-粘贴数 | 图像拼接数 | 图像删除数 |
---|---|---|---|---|
Columbia[ | 180 | 0 | 180 | 0 |
Coverage[ | 100 | 100 | 0 | 0 |
CASIA V2.0[ | 5 063 | 3 235 | 1 828 | 0 |
NIST16[ | 564 | 68 | 288 | 208 |
Tab. 1 Quantities of tampering operations in four datasets
数据集 | 图像数 | 复制-粘贴数 | 图像拼接数 | 图像删除数 |
---|---|---|---|---|
Columbia[ | 180 | 0 | 180 | 0 |
Coverage[ | 100 | 100 | 0 | 0 |
CASIA V2.0[ | 5 063 | 3 235 | 1 828 | 0 |
NIST16[ | 564 | 68 | 288 | 208 |
网络 | CASIA V2.0 | Coverage | NIST16 | Columbia | ||||
---|---|---|---|---|---|---|---|---|
AUC | F1 | AUC | F1 | AUC | F1 | AUC | F1 | |
ELA[ | 61.3 | 21.4 | 58.3 | 22.2 | 42.9 | 23.6 | — | — |
NOI1[ | 61.2 | 26.3 | 58.7 | 26.9 | 48.7 | 28.5 | 58.6 | — |
CFA1[ | 52.2 | 20.7 | 48.5 | 19.0 | 50.1 | 17.4 | 48.7 | — |
ManTra-Net[ | 81.7 | 48.1 | 79.5 | — | 84.5 | 82.0 | 82.4 | — |
MVSS-Net[ | 86.6 | 62.4 | 73.1 | 22.4 | 83.9 | 75.3 | 98.0 | 80.2 |
SPAN[ | 83.8 | 38.2 | 93.7 | 55.8 | 83.6 | 29.0 | — | — |
PSCC-Net[ | 87.5 | 55.4 | 94.1 | 72.3 | 99.6 | 81.9 | 98.2 | 98.1 |
ObjectFormer[ | 88.2 | 57.9 | 95.7 | 75.8 | 99.6 | 82.4 | 95.5 | — |
本文网络 | 89.1 | 68.3 | 81.5 | 65.1 | 93.8 | 90.2 | 99.3 | 99.1 |
Tab. 2 Pixel-level positioning AUC and F1 evaluation on test sets for existing fine-tuning models
网络 | CASIA V2.0 | Coverage | NIST16 | Columbia | ||||
---|---|---|---|---|---|---|---|---|
AUC | F1 | AUC | F1 | AUC | F1 | AUC | F1 | |
ELA[ | 61.3 | 21.4 | 58.3 | 22.2 | 42.9 | 23.6 | — | — |
NOI1[ | 61.2 | 26.3 | 58.7 | 26.9 | 48.7 | 28.5 | 58.6 | — |
CFA1[ | 52.2 | 20.7 | 48.5 | 19.0 | 50.1 | 17.4 | 48.7 | — |
ManTra-Net[ | 81.7 | 48.1 | 79.5 | — | 84.5 | 82.0 | 82.4 | — |
MVSS-Net[ | 86.6 | 62.4 | 73.1 | 22.4 | 83.9 | 75.3 | 98.0 | 80.2 |
SPAN[ | 83.8 | 38.2 | 93.7 | 55.8 | 83.6 | 29.0 | — | — |
PSCC-Net[ | 87.5 | 55.4 | 94.1 | 72.3 | 99.6 | 81.9 | 98.2 | 98.1 |
ObjectFormer[ | 88.2 | 57.9 | 95.7 | 75.8 | 99.6 | 82.4 | 95.5 | — |
本文网络 | 89.1 | 68.3 | 81.5 | 65.1 | 93.8 | 90.2 | 99.3 | 99.1 |
网络 | 帧率 |
---|---|
ManTra-Net | 2.8 |
MVSS-Net | 20.1 |
本文网络 | 12.9 |
Tab. 3 Comparison of algorithm complexity
网络 | 帧率 |
---|---|
ManTra-Net | 2.8 |
MVSS-Net | 20.1 |
本文网络 | 12.9 |
网络结构 | CASIA V2.0 | Coverage | NIST16 | Columbia | |||||
---|---|---|---|---|---|---|---|---|---|
AUC | F1 | AUC | F1 | AUC | F1 | AUC | F1 | ||
M5 | 77.0 | 59.2 | 75.6 | 53.9 | 89.8 | 83.1 | 94.1 | 87.2 | |
M1+M5 | 78.5 | 61.3 | 76.2 | 54.6 | 92.4 | 84.7 | 97.7 | 93.8 | |
M1+M2+M5 | 78.7 | 62.1 | 78.6 | 59.2 | 92.6 | 85.6 | 98.3 | 96.0 | |
M1+M2+M3+M5 | 80.2 | 64.6 | 80.3 | 63.8 | 92.8 | 87.5 | 98.9 | 97.5 | |
M1+M2+M3+M4+M5 | 89.1 | 68.3 | 81.5 | 65.1 | 93.8 | 90.2 | 99.3 | 99.1 |
Tab. 4 Positioning performance of network under different mask supervisions
网络结构 | CASIA V2.0 | Coverage | NIST16 | Columbia | |||||
---|---|---|---|---|---|---|---|---|---|
AUC | F1 | AUC | F1 | AUC | F1 | AUC | F1 | ||
M5 | 77.0 | 59.2 | 75.6 | 53.9 | 89.8 | 83.1 | 94.1 | 87.2 | |
M1+M5 | 78.5 | 61.3 | 76.2 | 54.6 | 92.4 | 84.7 | 97.7 | 93.8 | |
M1+M2+M5 | 78.7 | 62.1 | 78.6 | 59.2 | 92.6 | 85.6 | 98.3 | 96.0 | |
M1+M2+M3+M5 | 80.2 | 64.6 | 80.3 | 63.8 | 92.8 | 87.5 | 98.9 | 97.5 | |
M1+M2+M3+M4+M5 | 89.1 | 68.3 | 81.5 | 65.1 | 93.8 | 90.2 | 99.3 | 99.1 |
网络设置 | CASIA V2.0 | Coverage | NIST16 | Columbia | ||||
---|---|---|---|---|---|---|---|---|
AUC | F1 | AUC | F1 | AUC | F1 | AUC | F1 | |
单ResNet-50+3×3卷积 | 75.3 | 56.7 | 74.3 | 47.9 | 86.7 | 76.1 | 98.1 | 94.1 |
双ResNet-50+3×3卷积 | 78.6 | 62.8 | 76.1 | 53.4 | 87.3 | 78.9 | 98.6 | 94.2 |
单ResNet-50+双输入细微特征模块 | 79.4 | 63.9 | 80.1 | 59.5 | 93.6 | 89.1 | 99.3 | 96.0 |
双ResNet-50+双输入细微特征模块 | 89.1 | 68.3 | 81.5 | 65.1 | 93.8 | 90.2 | 99.3 | 99.1 |
Tab. 5 Ablation experiment results
网络设置 | CASIA V2.0 | Coverage | NIST16 | Columbia | ||||
---|---|---|---|---|---|---|---|---|
AUC | F1 | AUC | F1 | AUC | F1 | AUC | F1 | |
单ResNet-50+3×3卷积 | 75.3 | 56.7 | 74.3 | 47.9 | 86.7 | 76.1 | 98.1 | 94.1 |
双ResNet-50+3×3卷积 | 78.6 | 62.8 | 76.1 | 53.4 | 87.3 | 78.9 | 98.6 | 94.2 |
单ResNet-50+双输入细微特征模块 | 79.4 | 63.9 | 80.1 | 59.5 | 93.6 | 89.1 | 99.3 | 96.0 |
双ResNet-50+双输入细微特征模块 | 89.1 | 68.3 | 81.5 | 65.1 | 93.8 | 90.2 | 99.3 | 99.1 |
攻击类型 | 参数 | AUC值/% | ||
---|---|---|---|---|
ManTra-Net | SPAN | 本文网络 | ||
无操作 | — | 84.5 | 83.6 | 93.8 |
高斯滤波 | k=3 | 77.4 | 83.1 | 91.5 |
k=15 | 74.5 | 79.1 | 84.5 | |
高斯噪声 | σ=3 | 67.4 | 75.1 | 92.1 |
σ=15 | 58.5 | 67.2 | 85.7 | |
JPEG压缩 | QF= 100 | 77.9 | 83.5 | 93.6 |
QF= 50 | 74.3 | 80.6 | 90.1 |
Tab. 6 Model AUC value comparison to different post-processing methods on NIST16 dataset
攻击类型 | 参数 | AUC值/% | ||
---|---|---|---|---|
ManTra-Net | SPAN | 本文网络 | ||
无操作 | — | 84.5 | 83.6 | 93.8 |
高斯滤波 | k=3 | 77.4 | 83.1 | 91.5 |
k=15 | 74.5 | 79.1 | 84.5 | |
高斯噪声 | σ=3 | 67.4 | 75.1 | 92.1 |
σ=15 | 58.5 | 67.2 | 85.7 | |
JPEG压缩 | QF= 100 | 77.9 | 83.5 | 93.6 |
QF= 50 | 74.3 | 80.6 | 90.1 |
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