Overview of State-of-the-Art Watermarking Technologies. Majid Rabbani Eastman Kodak Company

Transcription

1 Overview of State-of-the-Art Watermarking Technologies Majid Rabbani Eastman Kodak Company

2 Data Embedding and Steganography Data embedding and watermarking techniques are particular embodiments of digital steganography, a composite word from the Greek words stegano (στεγανω) meaning covered, and graphos, meaning to write

3 Types of Watermarks Visible Invisible: perceptually transparent Fragile: (breaks down by slightest image alteration) Robust: (survives severe manipulation or tampering) Private: requires original or reference image for watermark detection Public (oblivious): does not require the original image

4 Applications of Watermarking Copyright and content protection Authentication and integrity verification Image tagging (tracing original that has been illegally copied) Security (e.g., passport photos) Metadata tagging (info about content, website address, etc.) Usage control Secret communication Many more.

5 Example of IBM s Visible Watermarking* *Downloaded from the IBM Website

6 Fragile Watermarking The slightest alteration of the image destroys the watermark. Used for authentication (at a pixel level or small block). Image X Watermark W Secret key Fragile Watermark Insertion Scheme Watermarked Image X Watermarked Image X Watermark W Secret (or Public) key Fragile Watermark Detection Scheme Authentication

7 Yueng Mintzer Fragile Watermarking (ref 1) A random key is used to build a lookup table that maps image pixel values to a binary value. The watermark is a binary image with the same dimensions as the original image (e.g., tiled version of a binary logo). For every pixel in the original image, the output of the LUT is found (with that pixel value as input) and compared to the watermark binary value at that location: If the same, the pixel value is left unchanged If different, the pixel value is altered by the smallest amount needed to create the desired output. The processed image replaces the original image as original.

8 Yueng Mintzer Fragile Watermarking Advantages Authentication is done on a pixel-by-pixel basis Disadvantages Cryptographic strength of the algorithm has not been widely studied - might be vulnerable to attacks. Difficult to adapt to applications where JPEG compression (or any other form of lossy compression) is used. The original image after watermarking can be perceptually different from the truly original image depending on the LUT used.

9 Example of Yeung-Mintzer Technique* Original Watermarked *Courtesy of M. Yeung and F. Mintzer, IBM.

10 Example of Yeung-Mintzer Technique* Watermarked, unaltered Watermarked, altered *Courtesy of M. Yeung and F. Mintzer, IBM.

11 Wong s Fragile Watermarking Technique (ref 2) Partition the image into blocks of n x n pixels (e.g., n = 8). For each block, strip-off the LSB of each pixel, Concatenate the remaining higher-order bits, the image size parameters, any metadata, and find its hashed value, Encrypt the first n 2 hashed values with the private key of a public-key encryption system (e.g., RSA), Exclusive OR the encrypted bitstream with a binary watermark, and Insert back the resulting bit stream in the least significant bit of the pixels in the image block.

12 Example of Wong s Fragile Watermarking* *Courtesy of P. Wong, Hewlett-Packard Company

13 Robust Watermarking Image X Watermark W Secret key Robust Watermark Insertion Scheme Watermarked Image X Watermarked Image X Secret (or Public) key Robust Watermark Extraction Extracted Watermark Watermarked Image X Watermark W Secret (or Public) key Robust Watermark Detection Yes or No

14 Robust Watermarking Desirable Properties Perceptual transparency Data capacity Robustness to unintentional image processing operations (e.g., compression, linear and nonlinear filtering, random noise, rotation and scale, cropping, analog conversion, etc.) Tamper resistance: difficulty for an attacker to destroy and remove, alter, or forge a message once it has been embedded as a watermark in an image. Computational complexity Need for an original (reference) image

15 Robust Watermarking Spatial domain techniques, examples include: MIT Patchwork (ref 3) Digimarc algorithm (ref 4) Kodak s algorithm Frequency domain techniques, examples include: Cox et al. spread spectrum technique (ref 5) IA-DCT and IA-W: perceptual watermarking of linear transforms (e.g., block DCT, wavelets) (ref 6)

16 The MIT Patchwork Technique A secret key is used to randomly select a subset of pixels in an image and then divide it into two distinct sets. The brightness of one set of pixels is shifted by a positive number, while the brightness of those in the other set are shifted by the corresponding negative number. Detection consists of partitioning the image into the corresponding sets using the secret key and computing the mean intensities of the pixels in each groups and finding their difference (for an unmarked image, the difference ~0). Only a limited amount of information can be embedded, even if the image is divided into several sets and a different bit is embedded in each set.

17 Watermarking Insertion Technique of Digimarc To embed N bits of information, N sequences of random numbers, each with the same dimension and extent as the original image, are generated (e.g., a user private key can be used to generate Gaussian numbers). The random numbers in each sequence are multiplied by their corresponding message bit values and added together to create a message image. The amplitude of the message image is attenuated down to an acceptable perceived noise amplitude to create the watermark image which is then added to the original image.

18 Digimarc s Watermarking Extraction Technique The original image is needed for watermark extraction, otherwise, small-signal detection techniques can be used to recover the watermark with a smaller probability of success. The image in question is first normalized both in scale and in RMS sense and is then subtracted from the original. The difference signal is cross-correlated with each of the N embedded message bits and the peak cross correlation value is recorded and used to decide the presence of 0 or 1. The code 0101 is always used as the first four bits, both as a calibrator (on the mean values of the 0 and 1, and on further registration of the two signals) and as an indication of the probable existence of the N-bit message.

19 Kodak s Robust Watermarking (Embedding) OR ICONIC BINARY Convolve Scale to small amplitude Flat spectrum / Random phase + Original image Data embedded image

20 Kodak s Robust Watermarking (Extraction) Sum of sections Flat spectrum / Random phase Correlate ICONIC Display OR BINARY Data embedded image

21 Example of Kodak s Robust Watermarking Original color image, 768x 512

22 Example of Kodak s Robust Watermarking Watermarked image bits embedded

23 Example of Kodak s Robust Watermarking Watermarked image with 20:1 JPEG compression bits extracted

24 Example of Kodak s Robust Watermarking Watermarked image with (20:1) JPEG + noise bits extracted

25 Example of Kodak s Robust Watermarking Watermarked image with JPEG + noise + scratches bits extracted

26 Example of Kodak s Robust Watermarking (20:1) JPEG + noise + scratches + 2-bit FS halftoning bits extracted

27 Cox et al. s Spread Spectrum Technique Compute the full-frame DCT of the original image and identify the perceptually significant coefficients (usually ~1000 coefficients) suitable for watermark embedding. Construct the watermark X = x 1, x 2,, x n, where each x i is chosen according to a Gaussian distribution with mean µ and variance σ 2, generated with a user private key. Insert the watermark in the DCT domain by modifying the DCT coefficient ν i according to : (α i is a scaling factor) ν i ν i (1+α i x i ). Compute the inverse DCT of the modified coefficients to construct a transparently marked image.

28 Cox s Technique Watermark Extraction Watermark detection requires access to the original image. Compute the DCT of a possibly watermarked image. Compute the DCT of the original image. Compute the difference between the results of the previous two steps and scale at each frequency according to ν i α i to create a potential watermark X*. Compare the original watermark X to X* using a similarity (correlation) measure defined by sim (X, X*) = X X* X* X*

29 Example of Cox s Robust Watermarking* Original Watermarked with α = 0.1 *Courtesy of C. Podilchuk, Bell Labs, Lucent Technologies

30 IA-DCT: Example of Perceptual Watermarking The image is partitioned into 8x8 blocks and the DCT of each block is computed (processing by blocks provides local control for incorporating local visual masking). Denoting by X u,v,b the DCT coefficients of the original image at position (u,v) of block b, by X* u,v,b the watermarked DCT coefficient, by W u,v,b the sequence of watermark values (generated according to a Gaussian distribution with a user secret key), and by J u,v,b the just noticeable differences computed from the original image, the insertion process is described as: X * u, v, b = X u, v, b + Ju, v, bwu, v, b, X u, v, b > J X u, v, b, otherwise u, v, b

31 IA-DCT Watermark Extraction The original image is required for best results (since the J values depend on the DCT coefficients in the original image), but authors claim that reasonable performance can also be obtained in the absence of the original. The detection scheme is similar to the spread spectrum technique and is based on classical detection theory. The image in question is subtracted from the original and the correlation between the signal difference and a specific watermark sequence is determined. The correlation value is compared to a threshold to determine presence/absence of the watermark.

32 Example of IA-DCT Robust Watermarking* IA-DCT watermarked (Original - Watermarked) scaled up *Courtesy of E. Delp, Purdue University & C. Podilchuk, Bell Labs, Lucent Technologies

33 Steganalysis Steganalysis is the science of attacking steganographic methods by detection, destruction, extraction, or alteration of the embedded data. It is assumed that the attacker understands the steganographic technique, so the effectiveness of the used method should lie in the selection of the of the setgo-key and not in the proprietary and secret nature of the method (Kerkhoff s principle in cryptography). stego-only known cover known message chosen stego chosen message

34 References 1 M. Yeung and F. Mintzer, An invisible watermarking technique for image verification, Proceedings of ICIP, Santa Barbara, CA, Oct , 1997, vol. 2, pp P. W. Wong, A public key watermark for image verification and authentication, Proceedings of ICIP, 1998, Chicago, pp W. Bender, D. Gruhl, and N. Morimoto, Techniques for data hiding, Technical Report, Massachusetts Institute of Technology Media Lab, G. B. Rhoads, Method and apparatus responsive to a code signal conveyed through a graphic image, US Patent 5,710,834, Issued January 20, I. Cox, J. Kilian, F. T. Leighton, and T. Shamoon, Secure spread spectrum watermarking for multimedia, IEEE Transactions on Image Processing, vol. 6, no. 12, December, 1997, pp R. B. Wolfgang, C. I. Podilchuk, and E. Delp, Perceptual watermarks for digital images and video, IEEE Proceedings, vol 87, no 7, July of M. D. Swanson, M. Kobayashi, and A. H. Tewfik, Multimedia data embedding and watermarking technologies, Proceedings of the IEEE, vol. 86, no. 6, June, 1998, pp R. Anderson and F. Petitcolas, On the limits of steganography, IEEE Journal on Selected Areas in Communications, vol. 16, no. 4, May, 1998, pp F. Mintzer, G. W. Braudaway and M. Yeung, Effective and ineffective digital watermarks, Proceedings of ICIP, October 26-29, 1997, Santa Barbara, CA, vol. 3, pp S. Craver, N. Memon, B.-L. Yeo and M. Yeung. Can invisible watermarks resolve rightful ownerships? Proceedings of the SPIE International Conference on Storage and Retrieval for Image and Video Databases V, Feb , 1997, San Jose, CA, vol. 3022, pp