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SCIENTIA SINICA Informationis, Volume 49, Issue 4: 464-485(2019) https://doi.org/10.1360/N112018-00196

A watermarking algorithm for image content authentication in double-compression environment

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  • ReceivedJul 28, 2018
  • AcceptedDec 17, 2018
  • PublishedApr 11, 2019

Abstract

Malicious tampering of digital image content may lead to serious consequences. As image resolution increases and its circulations on the network grows rapidly, images undergo double compression in the following environment: compression (JPEG/JPEG2000) for first release, decoding, application processing (conventional signal processing/security attacks/malicious content tampering), and compression (JPEG2000/JPEG) for rerelease before detection. Effectively determining whether the image content has been tampered and to locate the tampering position in a double-compression environment is an urgent problem. In this paper, a novel watermark expression method based on rotating vector and its modulation algorithm is proposed. Based on this method, a semi-fragile watermarking scheme is established for the image content authentication. The stability of watermarked data is analyzed theoretically, providing the watermark scheme with a theoretical basis. The authentication schemes with feature extraction and reconstruction, watermark embedding and extraction, and tamper detection and localization are mainly elaborated. The robustness of watermark, its security, and related detection performance are analyzed. Theoretical analysis and experiments show that the scheme features a good watermark transparency, good robustness, and stable distribution for different attacks and can effectively distinguish malicious tampering from content-preserving operators and locate tampering area under double-compression environment. In addition, the proposed scheme can resist watermark attacks, collage attacks, and forge attacks. Compared with related schemes, the proposed scheme has superior comprehensive performance and is suitable for content authentication in double-compression environment, which expands the application scope of watermark-based content authentication.


Funded by

浙江省自然科学基金(LY18F020003)

国家自然科学基金(61771270)

国家自然科学基金(61502415)


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  • Figure 1

    (Color online) Watermarked vector ${{\boldsymbol~A}_w}$ formed through rotating the original vector (${\boldsymbol~A}$) by $\theta$. (a) Counterclockwise; (b) clockwise

  • Figure 2

    (Color online) The relationship between the dithered angle ($\theta_d$) of vector ${\boldsymbol~A}$ and the cumulative probability under perturbation for (a) test schemes 1$\sim$4, respectively, and (b) test scheme 5 and schemes 1$\sim$4

  • Figure 3

    (Color online) The probability of feature stability for individual attack in double compression environment for (a) test schemes 1$\sim$4, respectively, and (b) test scheme 5 and schemes 1$\sim$4

  • Figure 4

    (Color online) Function relationship between tampering proportion ($p_t$) and robustness of the tampering matrix ($P_{\rm~t\_Tr}$), false positive probability for localization ($P_{\rm~tl\_fp}$), false negative probability for localization ($P_{\rm~tl\_fn}$) and tampering localization accuracy ($P_{\rm~tl\_d}$), respectively, under double

  • Figure 5

    (Color online) Robustness of single compression. (a) JPEG; (b) JPEG2000

  • Figure 6

    (Color online) Robustness of different image attacks during double compression. (a)$\sim$(d) correspond to the double compression schemes 1$\sim$4, respectively, (e) is the uncompressed case, and (f) is the average of (a)$\sim$(d)

  • Figure 7

    (Color online) Detection mask images after content authentication for the content-preserving image attack in the double compression environment. Subtitles show the detailed process of signal processing; “DCi" stands for ith-type double compression scheme; “Ai" is the number of attacks in Subsection 2.4. (a) Jpeg(75); (b) Jp2k(8); (c) DC1; (d) DC2; (e) DC3; (f) DC4; (g) DC5; (h) DC1$\leftarrow$A3; (i) DC2$\leftarrow$A6; (j) DC3$\leftarrow$A8; (k) DC4$\leftarrow$A11; (l) DC5$\leftarrow$A2

  • Figure 8

    (Color online) Detection mask images after content authentication for malicious content tampering attack in the double compression environment. Subtitles show the detailed process of signal processing; “DCi" stands for ith-type double compression scheme; “Ai" is the number of attacks in Subsection 2.4; “T" and “Tc" respectively represent content tampering and collage attack. (a) Jpeg(75)TcA1; (b) Jp2k(8)Tc; (c) DC1$\leftarrow$Tc; (d) DC2$\leftarrow$Tc; (e) DC3$\leftarrow$T; (f) DC4$\leftarrow$T; (g) DC5$\leftarrow$T; (h) DC1$\leftarrow$TA3; (i) DC2$\leftarrow$TA6; (j) DC3$\leftarrow$TA8; (k) DC4$\leftarrow$TA11; (l) DC5$\leftarrow$TA2

  • Table 1   Comparison of PSNR values
    Lena Sailboat Baboon Peppers Man Average (128 images)
    Ours 40.71 39.03 42.46 39.80 39.41 41.07
    Qi 40.28 40.30 40.35 40.33 40.32 40.30
    Wang 34.13 31.40 31.58 34.84 32.53 34.00
    Hsu 38.69 37.38 37.78 38.45 37.80 38.19

    a) The bold font represents the maximum of the corresponding column.

  • Table 2   Comparison of SSIM values
    Lena Sailboat Baboon Peppers Man Average (128 images)
    Ours 0.9861 0.9885 0.9960 0.9837 0.9862 0.9897
    Qi 0.9598 0.9707 0.9866 0.9609 0.9704 0.9701
    Wang 0.9620 0.9527 0.9659 0.9653 0.9480 0.9627
    Hsu 0.9563 0.9629 0.9826 0.9554 0.9631 0.9650

    a) The bold font represents the maximum of the corresponding column.

  • Table 3   Tampering detection results and comparison of theoretical and experimental data under double compression
    Sub-graph $p_t$ (%) $P_{\rm~t\_Tr}$ $P_{\rm~tl\_fp}$ $P_{\rm~tl\_fn}$ BER TCR Auth.
    Theory Experiment Theory Experiment Theory Experiment
    (a) 1.000 0.058 0.058 0.000 Y
    (b) 0.982 0.127 0.127 0.139 Y
    (c) 0.975 0.195 0.195 0.130 Y
    (d) 0.971 0.170 0.170 0.172 Y
    (e) 0.998 0.037 0.037 0.053 Y
    (I) (f) 0.952 0.180 0.182 0.266 Y?
    Figure 7 (g) 0.0 0.977 0.990 0.023 0.052 0.0 0.0 0.052 0.189 Y
    (h) 0.987 0.169 0.169 0.075 Y
    (i) 0.913 0.227 0.227 0.313 Y?
    (j) 1.000 0.046 0.046 0.000 Y
    (k) 0.969 0.175 0.175 0.179 Y
    (l) 0.957 0.218 0.218 0.197 Y
    Avg($\cdot$) $|\Delta|=0.002$ $|\Delta|=0.119$ $|\Delta|=0.0$ 0.142
    Sub-graph $p_t$ (%) $P_{\rm~t\_Tr}$ $P_{\rm~tl\_fp}$ $P_{\rm~tl\_fn}$ BER TCR Auth.
    Theory Experiment Theory Experiment Theory Experiment
    (a) 10.55 0.939 0.917 0.036 0.031 0.051 0.054 0.226 0.368 N
    (b) 16.41 0.913 0.894 0.043 0.050 0.076 0.107 0.259 0.411 N
    (c) 20.31 0.890 0.894 0.049 0.041 0.096 0.138 0.283 0.376 N
    (d) 24.90 0.857 0.868 0.057 0.067 0.121 0.185 0.296 0.446 N
    (e) 11.82 0.935 0.943 0.037 0.008 0.056 0.069 0.146 0.389 N
    (II) (f) 17.19 0.908 0.870 0.044 0.062 0.081 0.104 0.278 0.467 N
    Figure 8 (g) 22.85 0.870 0.885 0.054 0.010 0.111 0.123 0.243 0.474 N
    (h) 27.34 0.843 0.837 0.060 0.043 0.131 0.153 0.333 0.490 N
    (i) 7.91 0.953 0.899 0.032 0.075 0.035 0.054 0.301 0.334 N
    (j) 29.30 0.829 0.891 0.063 0.003 0.141 0.187 0.268 0.409 N
    (k) 40.43 0.749 0.758 0.078 0.060 0.197 0.222 0.380 0.638 N
    (l) 50.78 0.671 0.649 0.090 0.069 0.253 0.227 0.442 0.792 NW
    Avg($\cdot$) $|\Delta|=0.023$ $|\Delta|=0.023$ $|\Delta|=0.027$
  • 表 4表 1  

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