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Robust Image Hashing With Isomap and Saliency Map for Copy Detection

Published: 01 January 2023 Publication History
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  • Abstract

    Compression technology for representing image is on demand for efficiently processing images in the Big Data era. Image hashing is an effective compression technology for computing a short representation based on visual content of input image. Currently, most reported image hashing algorithms have weakness in making a desirable classification between discrimination and robustness and thus can not reach good performance in copy detection. To address these issues, this paper proposes a new robust image hashing with Isometric Mapping (Isomap) and saliency map for copy detection. A key contribution is hash generation with saliency map determined by the Frequency Tuned (FT) method, which can guarantee robustness of the proposed image hashing. Another contribution is the use of Isomap in deriving hash from the FT-based saliency map. Since Isomap can discover the internal geometry features of image, the use of Isomap can learn discriminative image features and thus discrimination of the proposed image hashing is ensured. Experiments on open image databases are carried out. Comparison results illustrate that the proposed image hashing is better than some state-of-the-art algorithms in the performances of classification and copy detection.

    References

    [1]
    Z. Zhou, Y. Wang, Q. M. J. Wu, C. Yang, and X. Sun, “Effective and efficient global context verification for image copy detection,”IEEE Trans. Inf. Forensic Secur., vol. 12, no. 1, pp. 48–63, Jan.2017.
    [2]
    Z. Huang and S. Liu, “Perceptual image hashing with texture and invariant vector distance for copy detection,”IEEE Trans. Multimedia., vol. 23, pp. 1516–1529, 2021.
    [3]
    Y. Hu, M. Liu, X. Su, Z. Gao, and L. Nie, “Video moment localization via deep cross-modal hashing,”IEEE Trans. Image Process., vol. 30, pp. 4667–4677, 2021.
    [4]
    S. Jin, H. Yao, Q. Zhou, Y. Liu, J. Huang, and X. Hua, “Unsupervised discrete hashing with affinity similarity,”IEEE Trans. Image Process., vol. 30, pp. 6130–6141, 2021.
    [5]
    L. Zhu, X. Lu, Z. Cheng, J. Li, and H. Zhang, “Deep collaborative multi-view hashing for large-scale image search,”IEEE Trans. Image Process., vol. 29, pp. 4643–4655, 2020.
    [6]
    Z. Tang, M. Yu, H. Yao, H. Zhang, C. Yu, and X. Q. Zhang, “Robust image hashing with singular values of quaternion SVD,”Comput. J., vol. 64, no. 11, pp. 1656–1671, 2021.
    [7]
    C. Kang, L. Zhu, X. Qian, J. Han, M. Wang, and Y. Tang, “Geometry and topology preserving hashing for SIFT feature,”IEEE Trans. Multimedia., vol. 21, pp. 1563–1576, 2019.
    [8]
    Z. Huang and S. Liu, “Perceptual hashing with visual content understanding for reduced-reference screen content image quality assessment,”IEEE Trans. Circuits Syst. Video Technol., vol. 31, no. 7, pp. 2808–2823, Jul.2021.
    [9]
    C. Qin, X. Chen, X. Luo, X. P. Zhang, and X. Sun, “Perceptual image hashing via dual-cross pattern encoding and salient structure detection,”Inf. Sci., vol. 423, pp. 284–302, Jan.2018.
    [10]
    Z. Tang, L. Chen, X. Q. Zhang, and S. Zhang, “Robust image hashing with tensor decomposition,”IEEE Trans. Knowl. Data Eng., vol. 31, no. 3, pp. 549–560, Mar.2019.
    [11]
    Q. Shen and Y. Zhao, “Perceptual hashing for color image based on color opponent component and quadtree structure,”Signal Process., vol. 166, Jan.2020, Art. no.
    [12]
    X. Liang, Z. Tang, X. Xie, J. Wu, and X. Q. Zhang, “Robust and fast image hashing with two-dimensional PCA,”Multimedia Syst., vol. 27, pp. 389–401, Jun.2021.
    [13]
    Y. Li and L. Guo, “Robust image fingerprinting via distortion-resistant sparse coding,”IEEE Signal Process. Lett., vol. 25, no. 1, pp. 140–144, Jan.2018.
    [14]
    A. Swaminathan, Y. Mao, and M. Wu, “Robust and secure image hashing,”IEEE Trans. Inf. Forensic Secur., vol. 1, no. 2, pp. 215–230, Jun.2006.
    [15]
    Z. Tang, X. Q. Zhang, X. Li, and S. Zhang, “Robust image hashing with ring partition and invariant vector distance,”IEEE Trans. Inf. Forensic Secur., vol. 11, no. 1, pp. 200–214, Jan.2016.
    [16]
    C. Kim, “Content-based image copy detection,”Signal Process Image Commun., vol. 18, no. 3, pp. 169–184, Mar.2003.
    [17]
    K. Yan and R. Sukthankar, “PCA-SIFT: A more distinctive representation for local image descriptors,” in Proc. IEEE Int. Conf. Comput. Vis. Pattern Recognit., 2004, pp. 506–513.
    [18]
    H. Ling, L. Yan, F. Zou, C. Liu, and H. Feng, “Fast image copy detection approach based on local fingerprint defined visual words,”Signal Process., vol. 93, no. 8, pp. 2328–2338, Aug.2013.
    [19]
    J. Yao, B. Yang, and Q. Zhu, “Near-duplicate image retrieval based on contextual descriptor,”IEEE Signal Process. Lett., vol. 22, no. 9, pp. 1404–1408, Sep.2015.
    [20]
    C.-S. Lu, C. Y. Hsu, S.-W. Sun, and P.-C. Chang, “Robust mesh-based hashing for copy detection and tracing of images,” in Proc. IEEE Int. Conf. Multimedia Expo., 2004, pp. 731–734.
    [21]
    Z. Tang, F. Yang, L. Huang, and X. Q. Zhang, “Robust image hashing with dominant DCT coefficients,”Optik- Int. J. Light Electron Opt., vol. 125, no. 18, pp. 5102–5107, Sep.2014.
    [22]
    S. Liu and Z. Huang, “Efficient image hashing with geometric invariant vector distance for copy detection,”ACM Trans. Multimedia Comput. Commun. Appl., vol. 15, no. 4, pp. 1–22, Jan.2020.
    [23]
    Z. Tang, Y. Dai, X. Q. Zhang, L. Huang, and F. Yang, “Robust image hashing via colour vector angles and discrete wavelet transform,”IET Image Process., vol. 8, no. 3, pp. 142–149, Mar.2014.
    [24]
    Z. Tang, Z. Huang, H. Yao, X. Q. Zhang, L. Chen, and C. Yu, “Perceptual image hashing with weighted DWT features for reduced-reference image quality assessment,”Comput. J., vol. 61, no. 11, pp. 1695–1709, Nov.2018.
    [25]
    S. M. Abdullahi, H. Wang, and T. Li, “Fractal coding-based robust and alignment-free fingerprint image hashing,”IEEE Trans. Inf. Forensic Secur., vol. 15, pp. 2587–2601, Feb.2020.
    [26]
    X. Lv and Z. J. Wang, “Perceptual image hashing based on shape contexts and local feature points,”IEEE Trans. Inf. Forensic Secur., vol. 7, no. 3, pp. 1081–1093, Jun.2012.
    [27]
    C. M. Pun, C. P. Yan, and X. C. Yuan, “Robust image hashing using progressive feature selection for tampering detection,”Multimed. Tools Appl., vol. 77, no. 10, pp. 11609–11633, May2017.
    [28]
    P. Wanget al., “Robust image hashing based on hybrid approach of scale-invariant feature transform and local binary patterns,” in Proc. IEEE Int. Conf. Digit. Signal Process., 2018, pp. 1–5.
    [29]
    M. Paul, R. K. Karsh, and F. Ahmed Talukdar, “Image hashing based on shape context and speeded up robust features (SURF),” in Proc. Int. Conf. Automat., Comput. Technol. Manage., 2019, pp. 464–468.
    [30]
    S. P. Singh, G. Bhatnagar, and A. K. Singh, “A new robust reference image hashing system,”IEEE Trans. Depend. Secure Comput., to be published.
    [31]
    Y. Zhao, S. Wang, X. P. Zhang, and H. Yao, “Robust hashing for image authentication using zernike moments and local features,”IEEE Trans. Inf. Forensics Secur., vol. 8, no. 1, pp. 55–63, Jan.2013.
    [32]
    J. Ouyang, X. Wen, J. Liu, and J. Chen, “Robust hashing based on quaternion zernike moments for image authentication,”ACM Trans. Multimedia Comput. Commun. Appl., vol. 12, no. 4, Nov.2016, Art. no.
    [33]
    K. Hosny, Y. Khedr, W. Khedr, and E. Mohamed, “Robust color image hashing using quaternion polar complex exponential transform for image authentication,”Circuits, Syst. Signal Process., vol. 37, no. 5, pp. 5441–5462, Dec.2018.
    [34]
    Z. Tang, X. Li, X. Q. Zhang, S. Zhang, and Y. Dai, “Image hashing with color vector angle,”Neurocomputing, vol. 308, no. 25, pp. 147–158, Sep.2018.
    [35]
    Z. Tang, Y. Yu, H. Zhang, M. Yu, C. Yu, and X. Q. Zhang, “Robust image hashing via visual attention model and ring partition,”Math. Biosci. Eng., vol. 16, no. 5, pp. 6103–6120, Jul.2019.
    [36]
    Y. Zhao and X. Yuan, “Perceptual image hashing based on color structure and intensity gradient,”IEEE Access, vol. 8, pp. 26041–26053, Jan.2020.
    [37]
    R. Davarzani, S. Mozaffari, and K. Yaghmaie, “Perceptual image hashing using center-symmetric local binary patterns,”Multimed. Tools Appl., vol. 75, no. 8, pp. 4639–4667, Apr.2016.
    [38]
    C. Qin, M. Sun, and C. Chang, “Perceptual hashing for color images based on hybrid extraction of structural features,”Signal Process., vol. 142, pp. 194–205, Jan.2018.
    [39]
    Z. Tang, H. Lao, X. Q. Zhang, and K. Liu, “Robust image hashing via DCT and LLE,”Comput. Secur., vol. 62, pp. 133–148, Sep.2016.
    [40]
    Z. Tang, X. Q. Zhang, and S. Zhang, “Robust perceptual image hashing based on ring partition and NMF,”IEEE Trans. Knowl. Data Eng., vol. 26, no. 3, pp. 711–724, Mar.2014.
    [41]
    Z. Tang, Z. Huang, X. Q. Zhang, and H. Lao, “Robust image hashing with multidimensional scaling,”Signal Process., vol. 137, pp. 240–250, Aug.2017.
    [42]
    F. Lefebvre, B. Macq, and J.-D. Legat, “RASH: Radon soft hash algorithm,” in Proc. Eur. Signal Process. Conf., 2002, pp. 299–302.
    [43]
    D. Wu, X. Zhou, and X. Niu, “A novel image hash algorithm resistant to print-scan,”Signal Process., vol. 89, no. 12, pp. 2415–2424, Dec.2009.
    [44]
    Y. Ou and K. H. Rhee, “A key-dependent secure image hashing scheme by using radon transform,” in Proc. IEEE Int. Symp. Intell. Signal Process. Commun. Syst., 2009, pp. 595–598.
    [45]
    Y. Lei, Y. Wang, and J. Huang, “Robust image hash in radon transform domain for authentication,”Signal Process.: Image Commun., vol. 26, no. 6, pp. 280–288, Jul.2011.
    [46]
    X. Huang, X. Liu, G. Wang, and M. Su, “A robust image hashing with enhanced randomness by using random walk on zigzag blocking,” in Proc. IEEE Trustcom/Bigdatase/Ispa, 2016, pp. 14–18.
    [47]
    C. Qin, Y. Hu, H. Yao, X. Duan, and L. Gao, “Perceptual image hashing based on weber local binary pattern and color angle representation,”IEEE Access, vol. 7, pp. 45460–45471, Mar.2019.
    [48]
    L. Kang, C. Lu, and C. Hsu, “Compressive sensing-based image hashing,” in Proc. IEEE Int. Conf. Image Process., 2009, pp. 1285–1288.
    [49]
    Y. Li, Z. Lu, C. Zhu, and X. Niu, “Robust image hashing based on random gabor filtering and dithered lattice vector quantization,”IEEE Trans. Image Process., vol. 21, no. 4, pp. 1963–1980, Apr.2012.
    [50]
    X. Wang, K. Pang, X. Zhou, Y. Zhou, L. Li, and J. Xue, “A visual model-based perceptual image hash for content authentication,”IEEE Trans. Inf. Forensic Secur., vol. 10, no. 7, pp. 1336–1349, Jul.2015.
    [51]
    C. Qin, E. Liu, G. Feng, and X. P. Zhang, “Perceptual image hashing for content authentication based on convolutional neural network with multiple constraints,”IEEE Trans. Circuits Syst. Video Technol., vol. 31, no. 11, pp. 4523–4537, Nov.2021.
    [52]
    R. Achanta, S. Hemami, F. Estrada, and S. Susstrunk, “Frequency-tuned salient region detection,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., 2009, pp. 1597–1604.
    [53]
    J. Tenenbaum, V. de Silva, and J. Langford, “A global geometric framework for nonlinear dimensionality reduction,”Sc., vol. 290, pp. 2319–2323, Dec.2000.
    [54]
    B. Yang, M. Xiang, and Y. Zhang, “Multi-manifold discriminant isomap for visualization and classification,”Pattern Recognit., vol. 55, pp. 215–230, Jul.2016.
    [55]
    R. Verma, P. Khurd, and C. Davatzikos, “On analyzing diffusion tensor images by identifying manifold structure using isomaps,”IEEE Trans. Med. Imag., vol. 26, no. 6, pp. 772–778, Jun.2007.
    [56]
    B. Li, Y. He, F. Guo, and L. Zuo, “A novel localization algorithm based on isomap and partial least squares for wireless sensor networks,”IEEE Trans. Instrum. Meas., vol. 62, no. 2, pp. 304–314, Feb.2013.
    [57]
    Kodak lossless true color image suite, 2013. Accessed: Apr. 15, 2017. [Online]. Available: http://r0k.us/graphics/kodak/
    [58]
    The pascal visual object classes 2012, 2012. Accessed: Jun. 8, 2020. [Online]. Available: http://host.robots.ox.ac.uk/pascal/VOC/
    [59]
    J. Wang, J. Li, and G. Wiederhold, “SIMPLIcity: Semantics-sensitive integrated matching for picture libraries,”IEEE Trans. Pattern Anal. Mach. Intell., vol. 23, no. 9, pp. 947–963, Sep.2001.
    [60]
    T. Fawcett, “An introduction to ROC analysis,”Pattern Recognit. Lett., vol. 27, no. 8, pp. 861–874, Jun.2006.
    [61]
    L. Itti, C. Koch, and E. Niebur, “A model of saliency-based visual attention for rapid scene analysis,”IEEE Trans. Pattern Anal. Mach. Intell., vol. 20, no. 11, pp. 1254–1259, Nov.1998.
    [62]
    X. Hou and L. Zhang, “Saliency detection: A spectral residual approach,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., 2007, pp. 1–8.
    [63]
    Y. Zhai and M. Shah, “Visual attention detection in video sequences using spatiotemporal cues,” in Proc. ACM Int. Conf. Multimedia, 2006, pp. 815–824.
    [64]
    M. Cheng, N. J. Mitra, X. Huang, P. H. S. Torr, and S. Hu, “Global contrast based salient region detection,”IEEE Trans. Pattern Anal. Mach. Intell., vol. 37, no. 3, pp. 569–582, Mar.2015.
    [65]
    T. Vikram, M. Tscherepanow, and M. B. Wrede, “A saliency map based on sampling an image into random rectangular regions of interest,”Pattern Recognit., vol. 45, no. 9, pp. 3114–3124, Sep.2012.

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    cover image IEEE Transactions on Multimedia
    IEEE Transactions on Multimedia  Volume 25, Issue
    2023
    8932 pages

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    IEEE Press

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    Published: 01 January 2023

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    • (2024)Robust Image Hashing via CP Decomposition and DCT for Copy DetectionACM Transactions on Multimedia Computing, Communications, and Applications10.1145/365011220:7(1-22)Online publication date: 1-Mar-2024
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