A Novel Scene-Based Video Watermarking Scheme for Copyright Protection

Abstract

Many illegal copies of original digital videos are being made, as they can be replicated perfectly through the Internet. Thus, it is extremely necessary to protect the copyrights of the owner and prevent illegal copying. This paper presents a novel approach to digital video watermarking for copyright protection using two different algorithms, whereby successive estimation of a statistical measure was used to detect scene boundaries and watermark was embedded in the detected scenes with discrete wavelet transform. Haar wavelet was used for decomposition. For embedding, the approaches used were (i) the detailed subband (LH subband) and (ii) the approximate subband (LL subband) of the cover video. Imperceptibility, robustness, and channel capacity were measured using both algorithms. The system was tested for robustness in the presence of 15 different attacks of five different categories, and, under multiple attacks, ensured that a wide spectrum of attack analysis has been done. The performance metrics measured included mean square error, peak signal-to-noise ratio, structural similarity index, normalized correlation, and bit error rate. The experimental results demonstrated the better visual imperceptibility and improved performance in terms of normalized correlation and bit error rate with embedding using the LL subband. Comparative analysis with existing schemes proved the improved robustness, better imperceptibility, and reduced computational time of both the proposed schemes.

Frame Selective and Dynamic Pattern Based Model for Effective and Secure Video Watermarking

“…The encouraging results were obtained in terms of robustness, imperceptibility and capacity of watermarking. Shukla et al [11] have applied the statistical measures to identify the scene change over the video. Once the scene is identified, the Haar wavelet was used with LH and LL sub bands to hide the watermark within video.…”

A Robust Watermarking Scheme Based on the Mean Modulation of DWT Coefficients

“…The first step in the block modulation phase is the partitioning of the confused host coefficients̃into independent blocks. This operation is realized by gathering each adjacent coefficients into a single block as given by (2), where is the index of the block and ‖ is the concatenation symbol. The total number of generated blocks should be larger or equal than the binary watermark message length.…”

“…Digital watermarking is the act of incorporating supplemental information into multimedia material (still images, audio, or video) in such a manner that it does not alter the delivered material and yet still readily exploited by the watermark detector, even after some undesirable operations. An overview of the literature reveals a broad range of application domains including encryption [1], copyright protection [2], integrity verification [3], content restoration [4], and documentation [5].…”

A robust video zero-watermarking based on deep convolutional neural network and self-organizing map in polar complex exponential transform domain

“…Joshi et al [6] combined discrete wavelet transform (DWT) and discrete cosine transform (DCT) with Arnold transform to achieve watermark robustness and imperceptibility by reason that energy concentration of DCT and multiresolution of DWT. Shukla and Sharma [22] presented a new watermarking method based on video scene, in which the detailed sub-band (LH sub-band) and the approximate sub-band (LL sub-band) of Haar wavelet are used for embedding. Hyperchaotic Lorentz system was used by Cao and Wang in video watermarking [2], which can improve security effectively.…”