A simple and efficient watermark technique based on JPEG2000 Codec

Abstract

In this paper we propose an effective watermark scheme for embedding and extracting based on the JPEG2000 Codec process. Our embedding algorithm applies the torus automorphisms (TA) technique to break up and scramble a watermark. The scrambled watermark was embedded into the quantized bitstreams of JPEG2000 before the entropy coding stage. Distortion reduction (DR) was applied to the compressed image to lessen image degradation caused by the embedding process. Our watermark scheme is simple and easy to implement. Furthermore, it is robust to attacks like blurring, edge enhancement, and other image processing manipulations.

A novel zero-watermarking scheme with enhanced distinguishability and robustness for volumetric medical imaging

“…Secondly, the size of our extracted feature is equal to that of watermark as shown in (23) to ensure the effective mapping between our extracted feature and the deployed watermark. Thirdly, the watermark size m × m is selected to 32 × 32 by considering the balance between computation complexity and amount of information for authenticity and copyright identification, which is a usual setting in watermarking applications such as [37,[49][50][51].…”

Visual Attention-Based Image Watermarking

“…Contradictory schemes embed data only within the low frequency subbands [54], [57], [61] aimed towards providing a high robustness. Spread spectrum domain embedding [59], [65]- [67] modifies data across all frequency subbands, ensuring a balance of both low and high frequency watermarking characteristics. The number of decomposition levels is also an important factor.…”

Quality Scalability Aware Watermarking for Visual Content

“…As the discrete wavelet transform (DWT) is the underlying technology of the JPEG2000 compression standard, we consider the watermarking schemes that use wavelet-based techniques in addition to the algorithms mentioned above. In these schemes, different approaches have been used as follows: a) Choosing Coefficients in a Specific Subband for Embedding the Watermark: e.g., embedding in high frequency subbands for better imperceptibility [7]- [10]; embedding in low frequency subband to achieve high robustness [11], [12]; and balancing imperceptibility and robustness with all subbands spread spectrum embedding [13], [14]. b) Using Different Wavelet Kernels: e.g., Haar or other Daubechies family orthogonal wavelets [8]- [11], [15] and biorthogonal wavelets [12].…”

“…b) Using Different Wavelet Kernels: e.g., Haar or other Daubechies family orthogonal wavelets [8]- [11], [15] and biorthogonal wavelets [12]. c) Optimising the Host Coefficient Selection: e.g., choosing all coefficients in a subband [8], [13]; using a threshold based on their magnitude significance [7]; the just noticeable difference(JND) [12]; a mask based on the Human Visual System (HVS) model [8]; a fusion rulebased mask for refining the selection of host coefficients [15] and blind re-quantization of a coefficient with respect to a group of coefficients within a given window [9]- [11], [14]. An in-depth review of the above schemes with respect to robustness to scalable content adaptation can be found in [24].…”