Volume 30 Issue 6
Nov.  2021
Turn off MathJax
Article Contents
CHEN Beijing, JU Xingwang, GAO Ye, WANG Jinwei. A Quaternion Two-Stream R-CNN Network for Pixel-Level Color Image Splicing Localization[J]. Chinese Journal of Electronics, 2021, 30(6): 1069-1079. doi: 10.1049/cje.2021.08.004
Citation: CHEN Beijing, JU Xingwang, GAO Ye, WANG Jinwei. A Quaternion Two-Stream R-CNN Network for Pixel-Level Color Image Splicing Localization[J]. Chinese Journal of Electronics, 2021, 30(6): 1069-1079. doi: 10.1049/cje.2021.08.004

A Quaternion Two-Stream R-CNN Network for Pixel-Level Color Image Splicing Localization

doi: 10.1049/cje.2021.08.004
Funds:

This work is supported by the National Natural Science Foundation of China (No.62072251, No.62072250) and the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD) Fund.

  • Received Date: 2021-04-14
  • Rev Recd Date: 2021-06-20
  • Available Online: 2021-09-23
  • Publish Date: 2021-11-05
  • Recently, Zhou et al. designed a twostream faster Region-Convolutional neural networks (RCNN) model RGB-N for color image splicing localization in CVPR2018. However, the RGB-N locates spliced regions only at block-level and ignores the entirety and inherent correlation of three channels. Therefore, an improved quaternion two-stream R-CNN model is proposed to solve these drawbacks:a mask branch combining fully convolutional network and condition random field is added for locating spliced regions at pixel-level; quaternion representation of color images is used to process color spliced images in a holistical way. In addition, feature pyramid network based on quaternion residual network is considered to extract multi-scale features for color spliced images; attention region proposal network is combined with attention mechanism and is designed to pay more attention to the spliced regions; a high-pass filter designed for image splicing detection specifically is adopted to replace steganalysis rich model filter in the RGB-N to obtain noise input for the noise stream. Experimental results on a new synthetic dataset and three standard forgery datasets demonstrate that the proposed method is superior to the existing methods in the abilities of localization, generalization, and robustness.
  • loading
  • Y. Lyu, X. Shen and H. Chen, "Copy-paste detection based on a SIFT marked graph feature vector", Chinese Journal of Electronics, Vol.26, No.2, pp.345-350, 2017.
    B. Chen, W. Tan, G. Coatrieux, et al., "A serial image copy-move forgery localization scheme with source/target distinguishment", IEEE Transactions on Multimedia. 2020. DOI:10.1109/TMM.2020.3026868, 2020.
    B. Xiao, J. X. Luo, X. L. Bi, et al., "Fractional discrete Tchebyshev moments and their applications in image encryption and watermarking", Information Sciences, Vol.516, pp.545-559, 2020.
    Y. Wu, W. AbdAlmageed and P. Natarajan, "ManTra-Net:Manipulation tracing network for detection and localization of image forgeries with anomalous features", Proc. of the 2019 IEEE Conference on Computer Vision and Pattern Recognition (CVPR2019), pp.9543-9552, 2019.
    H. Yao, H. Wei, C. Qin, et al., "An improved first quantization matrix estimation for nonaligned double compressed JPEG images", Signal Processing, Vol.170, DOI:10.1016/j.sigpro.2019.107430, 2020.
    C. Kumawat and V. Pankajakshan, "A robust JPEG compression detector for image forensics", Signal Processing:Image Communication, Vol.89, Article ID 116008, 13 pages, 2020.
    S. Lyu, X. Pan and X. Zhang, "Exposing region splicing forgeries with blind local noise estimation", International Journal of Computer Vision, Vol.110, No.2, pp.202-221, 2014.
    P. Ferrara, T. Bianchi, R. De, et al., "Image forgery localization via fine-grained analysis of CFA artifacts", IEEE Transactions on Information Forensics and Security, Vol.7, No.5, pp.1566-1577, 2012.
    D. Zhang, X. Wang, M. Zhang, et al., "Image splicing localization using noise distribution characteristic", Multimedia Tools and Applications, Vol.78, pp.22223-22247, 2019.
    S. Dua, J. Singh and H. Parthasarathy, "Detection and localization of forgery using statistics of DCT and Fourier components", Signal Processing:Image Communication, Vol.82, Article ID 115778, 18 pages, 2020.
    M. Johnson and H. Farid, "Exposing digital forgeries in complex lighting environments", IEEE Transactions on Information Forensics and Security, Vol.2, No.3, pp.450-461, 2007.
    H. Yao, S. Wang, Y. Zhao, et al., "Detecting image forgery using perspective constraints", IEEE Signal Processing Letters, Vol.19, No.7, pp.123-126, 2012.
    Q. Wang and R. Zhang, "Double JPEG compression forensics based on a convolutional neural network", EURASIP Journal on Information Security, Vol.2016, Article ID 23, 12 pages, 2016.
    D. Cozzolino, G. Poggi and L. Verdoliva, "Recasting residual-based local descriptors as convolutional neural networks:An application to image forgery detection", Proc. of the 5th ACM Workshop on Information Hiding and Multimedia Security, pp.159-164, 2017.
    Y. Liu, Q. Guan, X. Zhao, et al., "Image forgery localization based on multi-scale convolutional neural networks", Proc. of the 6th ACM Workshop on Information Hiding and Multimedia Security, pp.85-90, 2018.
    Y. Wei, X. Bi and B. Xiao, "C2R Net:The coarse to refined network for image forgery detection", Proc. of the 2018 IEEE International Conference on Trust, Security and Privacy in Computing and Communications, pp.1656-1659, 2018.
    B. Liu and C. Pun. "Locating splicing forgery by fully convolutional networks and conditional random field", Signal Processing:Image Communication, Vol.66, pp.103-112, 2018.
    R. Salloum, Y. Ren and C. Kuo, "Image splicing localization using a multi-task fully convolutional network (MFCN) ", Journal of Visual Communication and Image Representation, Vol.51, pp.201-209, 2018.
    B. Chen, Y. Gao, L. Xu, et al., "Color image splicing localization algorithm by quaternion fully convolutional networks and superpixel-enhanced pairwise conditional random field", Mathematical Biosciences and Engineering, Vol.16, No.6, pp.6907-6922, 2019.
    B. Chen, X. Qi, Y. Wang, et al., "An improved splicing localization method by fully convolutional networks", IEEE Access, Vol.6, pp.69472-69480, 2018.
    D. Cozzolino and L. Verdoliva, "Noiseprint:A CNN-based camera model fingerprint", IEEE Transactions on Information Forensics and Security, Vol.15, pp.144-159, 2020.
    B. Chen, X. Qi, Y. Zhou, et al., "Image splicing localization using residual image and residual-based fully convolutional network", Journal of Visual Communication and Image Representation, Vol.73, Article ID 102967, 9 pages, 2020.
    P. Zhou, X. Han, V. Morariu, et al., "Learning rich features for image manipulation detection", Proc. of the 2018 IEEE Conference on Computer Vision and Pattern Recognition (CVPR2018), pp.1053-1061, 2018.
    J. Chu, Z. Guo and L. Leng, "Object detection based on multi-layer convolution feature fusion and online hard example mining", IEEE Access, Vol.6, pp.19959-19967, 2018.
    K. He, G. Gkioxari, P. Dollár, et al., "Mask R-CNN", Proc. of the 2017 IEEE International Conference on Computer Vision (ICCV2017), pp.2961-2969, 2017.
    F. Wang, Z Li, Q. Liu, et al., "Fine pedestrian segmentation with parts detection and retrieval", Acta Electronica Sinica, Vol.47, No.2, pp.502-508, 2019. (in Chinese)
    T. Lin, P. Dollar, R. Girshick, et al., "Feature pyramid networks for object detection", Proc. of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR2017), pp.2117-2125, 2017.
    S. Zheng, S. Jayasumana, B. Romera-Paredes, et al., "Conditional random fields as recurrent neural networks", Proc. of the 2015 IEEE International Conference on Computer Vision (ICCV2015), pp.1529-1537, 2015.
    J. Fridrich and J. Kodovsky. "Rich models for steganalysis of digital images", IEEE Transactions on Information Forensics and Security, Vol.7, No.3, pp.868-882, 2012.
    Ö. Subakan and B. Vemuri, "A quaternion framework for color image smoothing and segmentation", International Journal of Computer Vision, Vol.91, No.3, pp.233-250, 2011.
    H. Abdulrahman, M. Chaumont, P. Montesinos, et al., "Color images steganalysis using RGB channel geometric transformation measures", Security and Communication Networks, Vol.9, No.15, pp.2945-2956, 2016.
    B. Chen, H. Shu, G. Coatrieux, et al., "Color image analysis by quaternion-type moments", Journal of Mathematical Imaging and Vision, Vol.51, No.1, pp.124-144, 2015.
    Z. Wang, J. Zhen, F. Zhu, et al., "Quaternion kernel fisher discriminant analysis for feature-level multimodal biometric recognition", Chinese Journal of Electronics, Vol.29, No.6, pp.1085-1092, 2020.
    F. Zhang, J. Li, G. Li, et al., "Video quality assessment based on quaternion singular value decomposition", Acta Electronica Sinica, Vol.39, No.1, pp.219-223, 2011. (in Chinese)
    S. Woo, J. Park, J. Lee, et al., "CBAM:Convolutional block attention module", Proc. of the 15th European Conference on Computer Vision (ECCV2018), pp.3-19, 2018.
    K. He, X. Zhang, S. Ren, et al., "Deep residual learning for image recognition", Proc. of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.770-778, 2016.
    D. Cozzolino, G. Poggi and L. Verdoliva, "Splicebuster:A new blind image splicing detector", Proc. of the 2015 IEEE International Workshop on Information Forensics and Security (WIFS2015), pp.1-6, 2015.
    T. Lin, M. Maire, S. Belongie, et al., "Microsoft coco:Common objects in context", Proc. of the 13th European Conference on Computer Vision (ECCV2014), pp.740-755, 2014.
    T. Lin, A. RoyChowdhury and S. Maji, "Bilinear CNN models for fine-grained visual recognition", Proc. of the 2015 IEEE International Conference on Computer Vision (ICCV2015), pp.1449-1457, 2015.
    J. Dong and W. Wang, "CASIA tampered image detection evaluation (TIDE) database, v1.0 and v2.0", Chinese Academy of Sciences, http://forensics.idealtest.org/,2020-03-06.
    Y. Hsu and S. Chang, "Detecting image splicing using geometry invariants and camera characteristics consistency", Proc. of the 2006 IEEE International Conference on Multimedia and Expo (ICME 2006), pp.549-552, 2006.
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Article Metrics

    Article views (122) PDF downloads(23) Cited by()
    Proportional views
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return