Journal of Computer Applications ›› 2023, Vol. 43 ›› Issue (9): 2925-2931.DOI: 10.11772/j.issn.1001-9081.2022081283
• Multimedia computing and computer simulation • Previous Articles Next Articles
Guolong YUAN, Yujin ZHANG(), Yang LIU
Received:
2022-08-28
Revised:
2022-10-31
Accepted:
2022-11-03
Online:
2023-01-11
Published:
2023-09-10
Contact:
Yujin ZHANG
About author:
YUAN Guolong, born in 1997, M. S. candidate. His research interests include image processing, image tampering forensics.Supported by:
通讯作者:
张玉金
作者简介:
袁国龙(1997—),男,安徽阜阳人,硕士研究生,CCF会员,主要研究方向:图像处理、图像篡改取证基金资助:
CLC Number:
Guolong YUAN, Yujin ZHANG, Yang LIU. Image tampering forensics network based on residual feedback and self-attention[J]. Journal of Computer Applications, 2023, 43(9): 2925-2931.
袁国龙, 张玉金, 刘洋. 基于残差反馈和自注意力的图像篡改取证网络[J]. 《计算机应用》唯一官方网站, 2023, 43(9): 2925-2931.
Add to citation manager EndNote|Ris|BibTeX
URL: https://www.joca.cn/EN/10.11772/j.issn.1001-9081.2022081283
数据集 | 训练集样本数 | 测试集样本数 |
---|---|---|
NIST16 | 404 | 160 |
COVERAGE | 75 | 25 |
CASIA | 5 123 | 921 |
Columbia | 135 | 45 |
Tab. 1 Information of different datasets
数据集 | 训练集样本数 | 测试集样本数 |
---|---|---|
NIST16 | 404 | 160 |
COVERAGE | 75 | 25 |
CASIA | 5 123 | 921 |
Columbia | 135 | 45 |
算法 | NIST16 | COVERAGE | CASIA v1.0 | Columbia |
---|---|---|---|---|
ELA | 23.6 | 22.2 | 21.4 | 47.0 |
NOI | 28.5 | 26.9 | 26.3 | 57.4 |
CFA1 | 17.4 | 19.0 | 20.7 | 46.7 |
RGB-N | 72.2 | 43.7 | 40.8 | 69.7 |
TED-Net | 61.0 | — | 44.0 | 85.0 |
本文算法 | 70.8 | 62.5 | 48.5 | 92.7 |
Tab. 2 Comparison of F1 scores on different datasets
算法 | NIST16 | COVERAGE | CASIA v1.0 | Columbia |
---|---|---|---|---|
ELA | 23.6 | 22.2 | 21.4 | 47.0 |
NOI | 28.5 | 26.9 | 26.3 | 57.4 |
CFA1 | 17.4 | 19.0 | 20.7 | 46.7 |
RGB-N | 72.2 | 43.7 | 40.8 | 69.7 |
TED-Net | 61.0 | — | 44.0 | 85.0 |
本文算法 | 70.8 | 62.5 | 48.5 | 92.7 |
算法 | NIST16 | COVERAGE | CASIA v1.0 | Columbia |
---|---|---|---|---|
ELA | 42.9 | 58.3 | 61.3 | 58.1 |
NOI | 48.7 | 58.7 | 61.2 | 54.6 |
CFA1 | 50.1 | 48.5 | 52.2 | 72.0 |
ManTra-Net | 79.5 | 81.9 | 81.7 | 82.4 |
RGB-N | 93.7 | 81.7 | 79.5 | 85.8 |
TED-Net | 96.0 | — | 83.0 | 87.0 |
本文算法 | 97.1 | 87.6 | 83.9 | 93.5 |
Tab. 3 Comparison of AUC values on different datasets
算法 | NIST16 | COVERAGE | CASIA v1.0 | Columbia |
---|---|---|---|---|
ELA | 42.9 | 58.3 | 61.3 | 58.1 |
NOI | 48.7 | 58.7 | 61.2 | 54.6 |
CFA1 | 50.1 | 48.5 | 52.2 | 72.0 |
ManTra-Net | 79.5 | 81.9 | 81.7 | 82.4 |
RGB-N | 93.7 | 81.7 | 79.5 | 85.8 |
TED-Net | 96.0 | — | 83.0 | 87.0 |
本文算法 | 97.1 | 87.6 | 83.9 | 93.5 |
模型 | NIST16 | Columbia | ||
---|---|---|---|---|
F1 | AUC | F1 | AUC | |
Base | 59.7 | 94.1 | 85.1 | 86.6 |
Base-RF | 61.3 | 94.0 | 86.2 | 87.1 |
Base-RF-RP | 67.1 | 95.4 | 91.3 | 92.9 |
Base-RF-RP-Adap | 70.5 | 96.9 | 92.5 | 93.5 |
本文算法 | 70.8 | 97.1 | 92.7 | 93.5 |
Tab. 4 Comparison of F1 scores and AUC values of different models on NIST16 and Columbia datasets
模型 | NIST16 | Columbia | ||
---|---|---|---|---|
F1 | AUC | F1 | AUC | |
Base | 59.7 | 94.1 | 85.1 | 86.6 |
Base-RF | 61.3 | 94.0 | 86.2 | 87.1 |
Base-RF-RP | 67.1 | 95.4 | 91.3 | 92.9 |
Base-RF-RP-Adap | 70.5 | 96.9 | 92.5 | 93.5 |
本文算法 | 70.8 | 97.1 | 92.7 | 93.5 |
压缩等级 | NIST16 | Columbia |
---|---|---|
100 | 0.708 | 0.927 |
90 | 0.603 | 0.795 |
70 | 0.571 | 0.742 |
50 | 0.568 | 0.735 |
Tab. 5 F1 scores of JPEG compression with different quality factors on NIST16 and Columbia datasets
压缩等级 | NIST16 | Columbia |
---|---|---|
100 | 0.708 | 0.927 |
90 | 0.603 | 0.795 |
70 | 0.571 | 0.742 |
50 | 0.568 | 0.735 |
1 | KRAWETZ N. A picture’s worth digital image analysis and forensics[C/OL] // Proceedings of the Black Hat Briefings USA 2007 [2022-06-22].. |
2 | MAHDIAN B, SAIC S. Using noise inconsistencies for blind image forensics[J]. Image and Vision Computing, 2009, 27(10): 1497-1503. 10.1016/j.imavis.2009.02.001 |
3 | FERRARA P, BIANCHI T, DE ROSA A, et al. Image forgery localization via fine-grained analysis of CFA artifacts[J]. IEEE Transactions on Information Forensics and Security, 2012, 7(5): 1566-1577. 10.1109/tifs.2012.2202227 |
4 | BAYAR B, STAMM M C. A deep learning approach to universal image manipulation detection using a new convolutional layer[C]// Proceedings of the 4th ACM Workshop on Information Hiding and Multimedia Security. New York: ACM, 2016: 5-10. 10.1145/2909827.2930786 |
5 | YANG Q W, PENG F, LI J T, et al. Image tamper detection based on noise estimation and lacunarity texture[J]. Multimedia Tools and Applications, 2016, 75(17): 10201-10211. 10.1007/s11042-015-3079-2 |
6 | BI X L, WEI Y, XIAO B, et al. RRU-Net: the ringed residual U-Net for image splicing forgery detection[C]// Proceedings of the 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops. Piscataway: IEEE, 2019:30-39. 10.1109/cvprw.2019.00010 |
7 | 吴鹏,陈北京,郑雨鑫,等. 基于双流Faster R-CNN的像素级图像拼接篡改定位算法[J]. 电子测量与仪器学报, 2021, 35(4):154-160. |
WU P, CHEN B J, ZHENG Y X, et al. Pixel-level image splicing localization algorithm based on dual-stream Faster R-CNN[J]. Journal of Electronic Measurement and Instrumentation, 2021, 35(4): 154-160. | |
8 | ZHONG J L, PUN C M. An end-to-end Dense-InceptionNet for image copy-move forgery detection[J]. IEEE Transactions on Information Forensics and Security, 2020 15: 2134-2146. 10.1109/tifs.2019.2957693 |
9 | 李应灿,杨建权,丁峰,等. 区分来源和目标区域的图像copy-move伪造检测方法[J]. 信号处理, 2020, 36(9):1533-1543. 10.16798/j.issn.1003-0530.2020.09.019 |
LI Y C, YANG J Q, DING F, et al. Copy-move detection method for distinguishing between source and target regions[J]. Journal of Signal Processing, 2020, 36(9):1533-1543. 10.16798/j.issn.1003-0530.2020.09.019 | |
10 | ZHU X S, QIAN Y J, ZHAO X F, et al. A deep learning approach to patch-based image inpainting forensics[J]. Signal Processing: Image Communication, 2018, 67: 90-99. 10.1016/j.image.2018.05.015 |
11 | WU Y, AbdALMAGEED W, NATARAJAN P. ManTra-Net: manipulation tracing network for detection and localization of image forgeries with anomalous features[C]// Proceedings of the 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2019: 9535-9544. 10.1109/cvpr.2019.00977 |
12 | BIACH F Z EL, IALA I, LAANAYA H, et al. Encoder-decoder based convolutional neural networks for image forgery detection[J]. Multimedia Tools and Applications, 2022, 81(16): 22611-22628. 10.1007/s11042-020-10158-3 |
13 | ZHUO L, TAN S Q, LI B, et al. Self-Adversarial training incorporating forgery attention for image forgery localization[J]. IEEE Transactions on Information Forensics and Security, 2022, 17: 819-834. 10.1109/tifs.2022.3152362 |
14 | BAPPY J H, SIMONS C, NATARAJ L, et al. Hybrid LSTM and encoder-decoder architecture for detection of image forgeries[J]. IEEE Transactions on Image Processing, 2019, 28(7): 3286-3300. 10.1109/tip.2019.2895466 |
15 | ZHOU P, HAN X T, MORARIU V I, et al. Learning rich features for image manipulation detection[C]// Proceedings of the 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2018: 1053-1061. 10.1109/cvpr.2018.00116 |
16 | MAZUMDAR A, BORA P K. Two-stream encoder-decoder network for localizing image forgeries[J]. Journal of Visual Communication and Image Representation, 2022, 82: No.103417. 10.1016/j.jvcir.2021.103417 |
17 | HE K M, ZHANG X Y, REN S Q, et al. Deep residual learning for image recognition[C]// Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2016: 770-778. 10.1109/cvpr.2016.90 |
18 | FU J, LIU J, TIAN H J, et al. Dual attention network for scene segmentation[C]// Proceedings of the 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2019: 3141-3149. 10.1109/cvpr.2019.00326 |
19 | ZHU Y, CHEN C F, YAN G, et al. AR-Net: adaptive attention and residual refinement network for copy-move forgery detection[J]. IEEE Transactions on Industrial Informatics, 2020, 16(10): 6714-6723. 10.1109/TII.2020.2982705 |
20 | nimble NIST 2016 datasets[DS/OL]. [2022-06-20].. |
21 | WEN B H, ZHU Y, SUBRAMANIAN R, et al. COVERAGE - a novel database for copy-move forgery detection[C]// Proceedings of the 2016 IEEE International Conference on Image Processing. Piscataway: IEEE, 2016: 161-165. 10.1109/icip.2016.7532339 |
22 | DONG J, WANG W, TAN T N. CASIA image tampering detection evaluation database[C]// Proceedings of the 2013 IEEE China Summit and International Conference on Signal and Information Processing. Piscataway: IEEE, 2013: 422-426. 10.1109/chinasip.2013.6625374 |
23 | NG T T, CHANG S F. A data set of authentic and spliced image blocks: DVENT Technical Report # 203-2004-3[R/OL]. (2004-06-08) [2022-06-23].. |
[1] | Zhigang XU, Chuang ZHANG. Multi-level color restoration of mural image based on gated positional encoding [J]. Journal of Computer Applications, 2024, 44(9): 2931-2937. |
[2] | Jing QIN, Zhiguang QIN, Fali LI, Yueheng PENG. Diagnosis of major depressive disorder based on probabilistic sparse self-attention neural network [J]. Journal of Computer Applications, 2024, 44(9): 2970-2974. |
[3] | Liting LI, Bei HUA, Ruozhou HE, Kuang XU. Multivariate time series prediction model based on decoupled attention mechanism [J]. Journal of Computer Applications, 2024, 44(9): 2732-2738. |
[4] | Zexin XU, Lei YANG, Kangshun LI. Shorter long-sequence time series forecasting model [J]. Journal of Computer Applications, 2024, 44(6): 1824-1831. |
[5] | Yue LIU, Fang LIU, Aoyun WU, Qiuyue CHAI, Tianxiao WANG. 3D object detection network based on self-attention mechanism and graph convolution [J]. Journal of Computer Applications, 2024, 44(6): 1972-1977. |
[6] | Rong HUANG, Junjie SONG, Shubo ZHOU, Hao LIU. Image aesthetic quality evaluation method based on self-supervised vision Transformer [J]. Journal of Computer Applications, 2024, 44(4): 1269-1276. |
[7] | Weina DONG, Jia LIU, Xiaozhong PAN, Lifeng CHEN, Wenquan SUN. High-capacity robust image steganography scheme based on encoding-decoding network [J]. Journal of Computer Applications, 2024, 44(3): 772-779. |
[8] | Xinran LUO, Tianrui LI, Zhen JIA. Chinese medical named entity recognition based on self-attention mechanism and lexicon enhancement [J]. Journal of Computer Applications, 2024, 44(2): 385-392. |
[9] | Ziqi HUANG, Jianpeng HU. Entity category enhanced nested named entity recognition in automotive domain [J]. Journal of Computer Applications, 2024, 44(2): 377-384. |
[10] | Liqing QIU, Xiaopan SU. Personalized multi-layer interest extraction click-through rate prediction model [J]. Journal of Computer Applications, 2024, 44(11): 3411-3418. |
[11] | Yanbo LI, Qing HE, Shunyi LU. Aspect sentiment triplet extraction integrating semantic and syntactic information [J]. Journal of Computer Applications, 2024, 44(10): 3275-3280. |
[12] | Xingyao YANG, Hongtao SHEN, Zulian ZHANG, Jiong YU, Jiaying CHEN, Dongxiao WANG. Sequential recommendation based on hierarchical filter and temporal convolution enhanced self-attention network [J]. Journal of Computer Applications, 2024, 44(10): 3090-3096. |
[13] | Jia CHEN, Hong ZHANG. Image text retrieval method based on feature enhancement and semantic correlation matching [J]. Journal of Computer Applications, 2024, 44(1): 16-23. |
[14] | Li’an CHEN, Yi GUO. Text sentiment analysis model based on individual bias information [J]. Journal of Computer Applications, 2024, 44(1): 145-151. |
[15] | Hanxiao SHI, Leichun WANG. Short-term power load forecasting by graph convolutional network combining LSTM and self-attention mechanism [J]. Journal of Computer Applications, 2024, 44(1): 311-317. |
Viewed | ||||||
Full text |
|
|||||
Abstract |
|
|||||