Adaptive Video Watermarking against Scaling Attacks Based on Quantization Index Modulation

Abstract

Video watermarking plays a vital role in protecting the video copyright. The quantization-based methods are widely used in the existing watermarking algorithms, owing to their low computational complexity and completely blind extraction. However, most of them work poorly in resisting scaling attacks, by which the quantization value may fall outside the original quantization interval. For addressing this issue, an adaptive quantization index modulation method is proposed. The property that is associated with the ratio of the DC coefficient before and after scaling the video resolution motivates us to select the DC coefficient as the quantization value and set the size of the quantization interval by the video resolution to maintain the synchronization between them before and after scaling. Moreover, a strategy taking advantage of the high decoding reliability of the QRCode is proposed to terminate the extraction in advance, and both the embedding and the extracting process are performed in the spatial domain, which all contribute to further enhance the execution efficiency. The experimental results show that our algorithm outperforms the state-of-the-art method in terms of imperceptibility, robustness, and computational cost.

A Robust Video Watermarking Algorithm Based on Two-Dimensional Discrete Fourier Transform

“…The definition of the inverse discrete fourier transform (IDFT) is shown in Equation ( 2). The linear property is one important property of 2D-DFT, which is shown in Equation (3), where G(u, v) is the corresponding 2D-DFT of g(x, y).…”

“…Consequently, we can first design two binary sequences in the 2D-DFT domain, called the zero-template and one-template, to represent watermark bits 0 and 1, respectively. Then, we can calculate the spatial mask corresponding to the watermark template by using Equation (3). Finally, watermark embedding can be conducted by adding the spatial mask to the pixel value of the cover video frame.…”

“…Next, we will take n = 3 as an example. Three bits means 2 3 templates are needed for embedding. We chose n1 = 2, pp 1 , pp 2 equal to 2 and 3, respectively.…”

“…The popularity and development of digital video technology brings convenience but also causes a series of problems, such as copyright infringement and fake information. Video data hiding, including video stenography [1,2] and video watermarking [3,4] has received extensive usage and continuous study. Here, we focus on video watermarking algorithms, which can hide copyright information in videos without being perceived and are more suitable for copyright protection.…”

Steganalysis of Compressed Speech Based on Association Rule Mining

“…The embedded secret information bits are obtained according to the partition index values of the output codewords. As an information hiding algorithm in compressed domain, QIM steganography is widely popularized to other various streaming media including speeches, such as, images [35], audios [36], videos [37], etc.. In view of of the QIM-based speech steganography, Xiao et al [29] proposed the complementary neighbor vertices (CNV) QIM-based algorithm for information embedding during the VQ process of LPC for the first time.…”