浏览全部资源
扫码关注微信
Published： 16 February 2024 ，
移动端阅览
张玲， 穆文鹏， 陈北京. 2024. 面向图像拼接检测的自适应残差算法. 中国图象图形学报， 29(02):0419-0429
Zhang Ling， Mu Wenpeng， Chen Beijing. 2024. Adaptive residual algorithm for image splicing detection. Journal of Image and Graphics， 29(02):0419-0429
目的
2
恶意的图像拼接篡改给名誉、法律、政治等带来一系列的挑战，而现有的图像拼接检测算法通常采用参数固定的高通滤波器提取滤波特征进行预处理，没有考虑图像之间的差异。
方法
2
本文设计自适应残差模块（adaptive residuals module， ARM）凸显拼接篡改痕迹，将卷积运算后的残差多次拼接，且每次拼接后再利用注意力机制实现通道间的非线性交互。然后，使用通道注意力SE（squeeze and excitation）模块以减少由ARM提取残差特征产生的通道之间信息冗余，并以在图像分类领域获得卓越性能的EfficientNet（high-efficiency network）为骨干网络，提出一种新的图像拼接检测算法。
结果
2
实验结果表明，所提算法在CASIA I（CASIA image tampering detection evaluation database），CASIA II，COLUMBIA COLOR，NIST16（NIST special database 16）和FaceForensic++这5个公开数据集上分别取得98.95%，98.88%，100%，100%，88.20%的检测准确率，获得比现有算法更高的准确率。提出的ARM将骨干网络EfficientNet在CASIA II 数据集的准确率提高了3.94%以上。
结论
2
提出的基于自适应残差的图像拼接检测算法充分考虑图像之间的差异，凸显篡改区域与未篡改区域之间的区别，并获得更好的拼接检测结果。
Objective
2
In recent years， digital media have become central to the exchange of information in our daily lives. With the rapid development of image editing tools and deep learning techniques， tampering with transmitted images is easy. Image splicing is one of the most common types of image tampering. Malicious image splicing challenges reputation， law， and politics. Therefore， various approaches have been proposed for detecting image splicing forgeries. Deep learning has also been successfully applied in image splicing detection. However， the existing deep learning-based works usually preprocess the input images by extracting features filtered by the high-pass filters with fixed parameters， which does not consider the differences between images.
Method
2
Therefore， a new image splicing detection algorithm is proposed in this paper. First， an adaptive residual module （ARM） is designed to highlight the splicing traces. In the ARM， the residual after the convolution operation is serialized several times， and the attention mechanism is used to realize the nonlinear interaction between channels after each connection. Unlike ordinary filters with fixed parameters， the ARM module entirely relies on the feature reuse and attention mechanism of residuals to retain and enlarge the details of the splicing. Then， a squeeze and excitation （SE） module is used to reduce the inter channel information redundancy generated by ARM residual feature extraction. The SE module uses an average adaptive pool to generate channel statistics information on global space and the gating mechanism of the Sigmoid activation function to learn channel weights from channel dependencies. Finally， a new image splicing detection algorithm is proposed by combining with the proposed ARM and the backbone network EffcientNet， a model with excellent performance in image classification.
Result
2
Experimental results show the proposed algorithm achieves 98.95%， 98.88%， 100%， 100%， and 88.20% detection accuracies on CASIA image tampering detection evaluation database（CASIA I）， CASIA II， COLUMBIA COLOR， NIST special database 16（NIST16）， and FaceForensic++， respectively， and obtains higher accuracy than the existing algorithms. Moreover， the proposed ARM algorithm improves the accuracy of backbone network by 3.94% on the CASIA II dataset. Regarding the computational time， on the CASIA II dataset， the training time per batch of the proposed algorithm is 71.75 s， and the test time for a single image is 0.011 s， which is less than the existing algorithms. In addition， the size of the parameters of ARM is 0.003 6 MB， which is about 2‰ of the parameters size of the backbone network EfficientNet， and the FLOPs are about 0.037 G.
Conclusion
2
This paper proposes an image splicing detection algorithm based on ARM， and the proposed algorithm performs well on five public datasets. The designed ARM is a plug-and-play lightweight， adaptive feature extraction module， and it can be migrated on other models， such as Xception and ResNet.
图像取证深度神经网络图像拼接检测自适应残差EfficientNet
image forensicsdeep neural networkimage splicing detectionadaptive residualEfficientNet
Abd El-Latif E I， Taha A and Zayed H H. 2020. A passive approach for detecting image splicing based on deep learning and wavelet transform. Arabian Journal for Science and Engineering， 45（4）： 3379-3386 ［DOI： 10.1007/S13369-020-04401-0http://dx.doi.org/10.1007/S13369-020-04401-0］
Ahmed M S and Naskar R. 2021. Image splicing detection and localisation using efficientnet and modified U-Net architecture//Proceedings of the 10th International Conference on Internet of Everything， Microwave Engineering， Communication and Networks （IEMECON）. Jaipur， India： IEEE： 1-5 ［DOI： 10.1109/IEMECON53809.2021.9689172http://dx.doi.org/10.1109/IEMECON53809.2021.9689172］
Bi X L， Wei Y， Xiao B and Li W S. 2019. RRU-Net： the ringed residual U-Net for image splicing forgery detection//Proceedings of 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops. Long Beach， USA： IEEE： 30-39 ［DOI： 10.1109/CVPRW.2019.00010http://dx.doi.org/10.1109/CVPRW.2019.00010］
Chen B J， Tan W J， Coatrieux G， Zheng Y H and Shi Y Q. 2021. A serial image copy-move forgery localization scheme with source/target distinguishment. IEEE Transactions on Multimedia， 23： 3506-3517 ［DOI： 10.1109/TMM.2020.3026868http://dx.doi.org/10.1109/TMM.2020.3026868］
Deng J K， Guo J， Ververas E， Kotsia I and Zafeiriou S. 2020. RetinaFace： single-shot multi-level face localisation in the wild//Proceedings of 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Seattle， USA： IEEE： 5203-5212 ［DOI： 10.1109/CVPR42600.2020.00525http://dx.doi.org/10.1109/CVPR42600.2020.00525］
Dong J， Wang W and Tan T N. 2013. CASIA image tampering detection evaluation database//Proceedings of 2013 IEEE China Summit and International Conference on Signal and Information Processing. Beijing， China： IEEE： 422-426 ［DOI： 10.1109/CHINASIP.2013.6625374http://dx.doi.org/10.1109/CHINASIP.2013.6625374］
Guo Z Q， Yang G B， Chen J Y and Sun X M. 2021. Fake face detection via adaptive manipulation traces extraction network. Computer Vision and Image Understanding， 204： #103170 ［DOI： 10.1016/J.CVIU.2021.103170http://dx.doi.org/10.1016/J.CVIU.2021.103170］
Hadwiger B C and Riess C. 2022. Deep metric color embeddings for splicing localization in severely degraded images. IEEE Transactions on Information Forensics and Security， 17： 2614-2627 ［DOI： 10.1109/TIFS.2022.3189527http://dx.doi.org/10.1109/TIFS.2022.3189527］
He P S， Li W C， Zhang J Y， Wang H X and Jiang X H. 2022. Overview of passive forensics and anti-forensics techniques for GAN-generated image. Journal of Image and Graphics， 27（1）： 88-110
何沛松， 李伟创， 张婧媛， 王宏霞， 蒋兴浩. 2022. 面向GAN生成图像的被动取证及反取证技术综述. 中国图象图形学报， 27（1）： 88-110 ［DOI： 10.11834/jig.210430http://dx.doi.org/10.11834/jig.210430］
Hsu Y F and Chang S F. 2006. Detecting image splicing using geometry invariants and camera characteristics consistency//Proceedings of 2006 IEEE International Conference on Multimedia and Expo. Toronto， Canada： IEEE： 549-552 ［DOI： 10.1109/ICME.2006.262447http://dx.doi.org/10.1109/ICME.2006.262447］
Hu J， Shen L and Sun G. 2018. Squeeze-and-excitation networks//Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City， USA： IEEE： 7132-7141 ［DOI： 10.1109/CVPR.2018.00745http://dx.doi.org/10.1109/CVPR.2018.00745］
Huang G， Liu Z， Van Der Maaten L and Weinberger K Q. 2017. Densely connected convolutional networks//Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition. Honolulu， USA： IEEE： 2261-2269 ［DOI： 10.1109/CVPR.2017.243http://dx.doi.org/10.1109/CVPR.2017.243］
Hublovský Š， Trojovský P， Bacanin N and Venkatachalam K. 2022. Evaluation of deepfake detection using YOLO with local binary pattern histogram. PeerJ Computer Science， 8： #1086 ［DOI： 10.7717/PEERJ-CS.1086http://dx.doi.org/10.7717/PEERJ-CS.1086］
Jain I and Goel N. 2021. Advancements in image splicing and copy-move forgery detection techniques： a survey//Proceedings of the 11th International Conference on Cloud Computing， Data Science and Engineering. Noida， India： IEEE： 470-475 ［DOI： 10.1109/Confluence51648.2021.9377104http://dx.doi.org/10.1109/Confluence51648.2021.9377104］
Jalab H A， Alqarni M A， Ibrahim R W and Ali Almazroi A. 2022. A novel pixel’s fractional mean-based image enhancement algorithm for better image splicing detection. Journal of King Saud University Science， 34（2）： #101805 ［DOI： 10.1016/J.JKSUS.2021.101805http://dx.doi.org/10.1016/J.JKSUS.2021.101805］
Lin T Y， Maire M， Belongie S， Hays J， Perona P， Ramanan D， Dollr P and Zitnick C L. 2014. Microsoft COCO： common objects in context//Proceedings of the 13th European Conference on Computer Vision. Zurich， Switzerland： Springer： 740-755 ［DOI： 10.1007/978-3-319-10602-1_48http://dx.doi.org/10.1007/978-3-319-10602-1_48］
Lin Z C， He J F， Tang X O and Tang C K. 2009. Fast， automatic and fine-grained tampered JPEG image detection via DCT coefficient analysis. Pattern Recognition， 42（11）： 2492-2501 ［DOI： 10.1016/J.PATCOG.2009.03.019http://dx.doi.org/10.1016/J.PATCOG.2009.03.019］
Liu H G， Li X D， Zhou W B， Chen Y F， He Y， Xue H， Zhang W M and Yu N H. 2021. Spatial-phase shallow learning： rethinking face forgery detection in frequency domain//Proceedings of 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Nashville， USA： IEEE： 772-781 ［DOI： 10.1109/CVPR46437.2021.00083http://dx.doi.org/10.1109/CVPR46437.2021.00083］
Mahdian B and Saic S. 2008. Detection of resampling supplemented with noise inconsistencies analysis for image forensics//Proceedings of 2008 International Conference on Computational Sciences and Its Applications. Perugia， Italy： IEEE： 546-556 ［DOI： 10.1109/ICCSA.2008.34http://dx.doi.org/10.1109/ICCSA.2008.34］
Nath S and Naskar R. 2021. Automated image splicing detection using deep CNN-learned features and ANN-based classifier. Signal， Image and Video Processing， 15（7）： 1601-1608 ［DOI： 10.1007/s11760-021-01895-5http://dx.doi.org/10.1007/s11760-021-01895-5］
Pomari T， Ruppert G， Rezende E， Rocha A and Carvalho T. 2018. Image splicing detection through illumination inconsistencies and deep learning//Proceedings of the 25th IEEE International Conference on Image Processing. Athens， Greece： IEEE： 3788-3792 ［DOI： 10.1109/ICIP.2018.8451227http://dx.doi.org/10.1109/ICIP.2018.8451227］
Rao Y and Ni J Q. 2016. A deep learning approach to detection of splicing and copy-move forgeries in images//Proceedings of 2016 IEEE International Workshop on Information Forensics and Security. Abu Dhabi， United Arab Emirates： IEEE： #7823911 ［DOI： 10.1109/WIFS.2016.7823911http://dx.doi.org/10.1109/WIFS.2016.7823911］
Rossler A， Cozzolino D， Verdoliva L， Riess C， Thies J and Nießner M. 2019. FaceForensics+<math id="M6"><mo>+</mo></math>https://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=53832868&type=https://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=53832865&type=1.354666712.03200006： learning to detect manipulated facial images//Proceedings of 2019 IEEE/CVF International Conference on Computer Vision. Seoul， Korea （South）： IEEE： 1-11 ［DOI： 10.1109/ICCV.2019.00009http://dx.doi.org/10.1109/ICCV.2019.00009］
Shang Z H， Xie H T， Zha Z， Yu L Y， Li Y and Zhang Y D. 2021. PRRNet： pixel-region relation network for face forgery detection. Pattern Recognition， 116： #107950 ［DOI： 10.1016/J.PATCOG.2021.107950http://dx.doi.org/10.1016/J.PATCOG.2021.107950］
Sun P， Lang Y B， Gong J C and Shen Z. 2017. Authentication method for splicing manipulation with inconsistencies in color shift. Journal of Computer-Aided Design and Computer Graphics， 29（8）： 1408-1415
孙鹏， 郎宇博， 巩家昌， 沈喆. 2017. 拼接篡改伪造图像的色彩偏移量不一致取证方法. 计算机辅助设计与图形学学报， 29（8）： 1408-1415
Tan M X and Le Q. 2019. EfficientNet： rethinking model scaling for convolutional neural networks//Proceedings of the 36th International Conference on Machine Learning. Long Beach， USA： PMLR： 6105-6114
Wang W， Dong J and Tan T N. 2009. Effective image splicing detection based on image chroma//Proceedings of the 16th IEEE International Conference on Image Processing. Cairo， Egypt： IEEE： 1257-1260 ［DOI： 10.1109/ICIP.2009.5413549http://dx.doi.org/10.1109/ICIP.2009.5413549］
Wang X F， Pang K M， Zhou X R， Zhou Y， Li L and Xue J R. 2015. A visual model-based perceptual image hash for content authentication. IEEE Transactions on Information Forensics and Security， 10（7）： 1336-1349 ［DOI： 10.1109/TIFS.2015.2407698http://dx.doi.org/10.1109/TIFS.2015.2407698］
Yan C P， Pun C M and Yuan X C. 2016. Quaternion-based image hashing for adaptive tampering localization. IEEE Transactions on Information Forensics and Security， 11（12）： 2664-2677 ［DOI： 10.1109/TIFS.2016.2594136http://dx.doi.org/10.1109/TIFS.2016.2594136］
Yang S C， Wang J， Sun Y L and Tang J H. 2022. Multi-level features global consistency for human facial deepfake detection. Journal of Image and Graphics， 27（9）： 2708-2720
杨少聪， 王健， 孙运莲， 唐金辉. 2022. 多级特征全局一致性的伪造人脸检测. 中国图象图形学报， 27（9）： 2708-2720 ［DOI： 10.11834/jig.211254http://dx.doi.org/10.11834/jig.211254］
Ye S M， Sun Q B and Chang E C. 2007. Detecting digital image forgeries by measuring inconsistencies of blocking artifact//Proceedings of 2007 IEEE International Conference on Multimedia and Expo. Beijing， China： IEEE： 12-15 ［DOI： 10.1109/ICME.2007.4284574http://dx.doi.org/10.1109/ICME.2007.4284574］
Zhang J Y， Wang H X and He P S. 2023. Multitask Transformer-based network for image splicing manipulation detection. Computer Science， 50（1）： 114-122
张婧媛， 王宏霞， 何沛松. 2023. 基于Transformer的多任务图像拼接篡改检测算法. 计算机科学， 50（1）： 114-122 ［DOI： 10.11896/JSJKX.211100269http://dx.doi.org/10.11896/JSJKX.211100269］
Zhang R Y and Ni J Q. 2020. A dense U-Net with cross-layer intersection for detection and localization of image forgery//Proceedings of 2020 IEEE International Conference on Acoustics， Speech and Signal Processing. Barcelona， Spain： IEEE： 2982-2986 ［DOI： 10.1109/ICASSP40776.2020.9054068http://dx.doi.org/10.1109/ICASSP40776.2020.9054068］
Zhang X， Hu X Y， Chen C and Peng S L. 2019. Image splicing detection based on spatial lighting consistency analysis under perspective projection. Acta Automatica Sinica， 45（10）： 1857-1869
张旭， 胡晰远， 陈晨， 彭思龙. 2019. 基于透视投影下空间光照一致性分析的图像拼接篡改检测. 自动化学报， 45（10）： 1857-1869 ［DOI： 10.16383/J.AAS.C190202http://dx.doi.org/10.16383/J.AAS.C190202］
Zhang Y L， Wang H X， Zhang R and Zhang J Y. 2023. Semantic consistency-relevant multitask splicing-tampered detection. Journal of Image and Graphics， 28（3）： 775-788
张玉林， 王宏霞， 张瑞， 张婧媛. 2023. 语义一致性引导的多任务拼接篡改检测. 中国图象图形学报， 28（3）： 775-788 ［DOI： 10.11834/jig.220549http://dx.doi.org/10.11834/jig.220549］
Zhao X D， Wang S L， Li S H and Li J H. 2015. Passive image-splicing detection by a 2-D noncausal markov model. IEEE Transactions on Circuits and Systems for Video Technology， 25（2）： 185-199 ［DOI： 10.1109/TCSVT.2014.2347513http://dx.doi.org/10.1109/TCSVT.2014.2347513］
Zhu X T， Tang Y Q and Geng P Z. 2021. Detection algorithm of tamper and deepfake image based on feature fusion. Netinfo Security， （8）： 70-81
朱新同， 唐云祁， 耿鹏志. 2021. 基于特征融合的篡改与深度伪造图像检测算法. 信息网络安全， （8）： 70-81 ［DOI： 10.3969/J.ISSN.1671-1122.2021.08.009http://dx.doi.org/10.3969/J.ISSN.1671-1122.2021.08.009］
相关文章
相关作者
相关机构
京公网安备11010802024621