A high capacity and strong robust fingerprinting for compressed images

Digital fingerprinting could trace the data source of illegal distribution effectively. Most existing algorithms are only adapted to uncompressed images, whose application fields are limited. In the paper a digital fingerprinting algorithm based on non-subsampled contourlet transform (NSCT) for compressed images is proposed. It is devoted to high capacity and strong robustness for compressed images fingerprinting. The NSCT low frequency coefficients of compressed images are more suitable for hiding information than DCT coefficients, and they are used to construct the high dimension host vector to hide Gaussian fingerprints. Through increasing the dimension of the host vector, on one hand the fingerprinting capacity improves fundamentally, on the other hand the ability of anti-collusion attack enhances greatly. Large experimental results shown that the proposed algorithm proves the declared performance compared with the existing algorithms.

[1]  Farrokh Marvasti,et al.  Contourlet-Based Image Watermarking Using Optimum Detector in a Noisy Environment , 2010, IEEE Transactions on Image Processing.

[2]  Min Wu,et al.  Fingerprinting Compressed Multimedia Signals , 2009, IEEE Transactions on Information Forensics and Security.

[3]  Min Wu,et al.  Anti-collusion forensics of multimedia fingerprinting using orthogonal modulation , 2005, IEEE Transactions on Image Processing.

[4]  Hongtao Lu,et al.  Robust watermarking based on DWT and nonnegative matrix factorization , 2009, Comput. Electr. Eng..

[5]  Fanjie Meng,et al.  A novel fingerprinting algorithm with blind detection in DCT domain for images , 2011 .

[6]  Ingemar J. Cox,et al.  Secure spread spectrum watermarking for multimedia , 1997, IEEE Trans. Image Process..

[7]  Reginald L. Lagendijk,et al.  Optimal differential energy watermarking of DCT encoded images and video , 2001, IEEE Trans. Image Process..

[8]  Yacov Yacobi,et al.  Improved Boneh-Shaw Content Fingerprinting , 2001, CT-RSA.

[9]  Jörg Schwenk,et al.  Combining digital watermarks and collusion-secure fingerprints for digital images , 1999, Electronic Imaging.

[10]  Minh N. Do,et al.  The Nonsubsampled Contourlet Transform: Theory, Design, and Applications , 2006, IEEE Transactions on Image Processing.

[11]  Min Wu,et al.  Exploring QIM-based anti-collusion fingerprinting for multimedia , 2006, Electronic Imaging.

[12]  Joseph W. Goodman,et al.  A mathematical analysis of the DCT coefficient distributions for images , 2000, IEEE Trans. Image Process..

[13]  Bernd Meyer,et al.  Cryptographic methods for collusion-secure fingerprinting of digital data , 2002, Comput. Electr. Eng..

[14]  Amit Phadikar,et al.  Region based QIM digital watermarking scheme for image database in DCT domain , 2011, Comput. Electr. Eng..

[15]  Dan Boneh,et al.  Collusion-Secure Fingerprinting for Digital Data , 1998, IEEE Trans. Inf. Theory.

[16]  Jeng-Shyang Pan,et al.  Rotation invariant watermark embedding based on scale-adapted characteristic regions , 2010, Inf. Sci..

[17]  Min Wu,et al.  Forensic analysis of nonlinear collusion attacks for multimedia fingerprinting , 2005, IEEE Transactions on Image Processing.

[18]  Jörg Schwenk,et al.  Combining digital watermarks and collusion secure fingerprints for digital images , 2000, J. Electronic Imaging.

[19]  Min Wu,et al.  Anti-collusion fingerprinting for multimedia , 2003, IEEE Trans. Signal Process..

[20]  Soumya Banerjee,et al.  Digital Watermarking using Ant Colony Optimization in Fractional Fourier Domain , 2010, J. Inf. Hiding Multim. Signal Process..

[21]  Bernd Girod,et al.  Watermarking of uncompressed and compressed video , 1998, Signal Process..

[22]  Minh N. Do,et al.  Ieee Transactions on Image Processing the Contourlet Transform: an Efficient Directional Multiresolution Image Representation , 2022 .