Automatic stereoscopic video object-based watermarking using qualified significant wavelet trees

Abstract

In this paper a fully automatic scheme for embedding visually recognizable watermark patterns to video objects is proposed. The architecture consists of 3 main modules. During the first module unsupervised video object extraction is performed, by analyzing stereoscopic pairs of frames. In the second module each video object is decomposed into three levels with ten subbands, using the Shape Adaptive Discrete Wavelet Transform (SA-DWT) and three pairs of subbands are formed (HL 3 , HL 2 ), (LH 3 , LH 2 ) and (HH 3 , HH 2 ). Next Qualified Significant Wavelet Trees (QSWTs) are estimated for the specific pair of subbands with the highest energy content. QSWTs are derived from the Embedded Zerotree Wavelet (EZW) algorithm and they are high-energy paths of wavelet coefficients. Finally during the third module, visually recognizable watermark patterns are redundantly embedded to the coefficients of the highest energy QSWTs and the inverse SA-DWT is applied to provide the watermarked video object. Performance of the proposed video object watermarking system is tested under various signal distortions such as JPEG lossy compression, sharpening, blurring and adding different types of noise. Furthermore the case of transmission losses for the watermarked video objects is also investigated. Experimental results on real life video objects indicate the efficiency and robustness of the proposed scheme.

Video-Object Steganography Mechanism to Overcome Compression Loss in Biometric Based Authentication

“…Initially the extracted host object is decomposed into two levels by the separable 2-D wavelet transform, providing three pairs of sub bands (HL2, HL1), (LH2, LH1) and (HH2,HH1). Afterwards, the pair of sub bands with the highest energy content is detected and a QSWTs approach is incorporated, in order to select the coefficients where the encrypted biometric signal should be casted [8] [14]. Finally, the signal is redundantly embedded to both sub bands of the selected pair, using a non-linear energy adaptable insertion procedure.…”

Video-Object Oriented Biometrics Hiding for User Authentication under Error-Prone Transmissions

“…More specifically, initially the extracted host object is decomposed into two levels by the separable 2-D wavelet transform, providing three pairs of subbands (HL 2 , HL 1 ), (LH 2 , LH 1 ), and (HH 2 , HH 1 ). Afterwards, the pair of subbands with the highest energy content is detected, and a QSWTs approach is incorporated [32] in order to select the coefficients where the encrypted biometric signal should be casted. Finally, the signal is redundantly embedded to both subbands of the selected pair, using a nonlinear energy-adaptable insertion procedure.…”