Robust information hiding in low-resolution videos with quantization index modulation in DCT-CS domain

Abstract

Video information hiding and transmission over noisy channels leads to errors on video and degradation of the visual quality notably. In this paper, a video signal fusion scheme is proposed to combine sensed host signal and the hidden signal with quantization index modulation (QIM) technology in the compressive sensing (CS) and discrete cosine transform (DCT) domain. With quantization based signal fusion, a realistic solution is provided to the receiver, which can improve the reconstruction video quality without requiring significant extra channel resource. The extensive experiments have shown that the proposed scheme can effectively achieve the better trade-off between robustness and statistical invisibility for video information hiding communication. This will be extremely important for low-resolution video analytics and protection in big data era

Adaptive Video Watermarking against Scaling Attacks Based on Quantization Index Modulation

“…Watermarking can be executed in a spatial domain [4,13,23,28,29] or a transform domain [3,[5][6][7][8][9][10][11][12]14,15,18,19,21,22,[24][25][26][27][30][31][32][33][34][35]. The spatial domain watermarking has high efficiency by modifying the pixels of the host images or frames directly to embed the watermark.…”

“…The popularity of the Internet makes video infringement more rampant. Video watermarking [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15], the process of embedding the copyright information called a watermark into the videos and then extracting the watermark to identify their ownership, can effectively protect the copyrights of the videos.…”

“…After determining the embedding domain, we should modify or analyze its coefficients to embed and extract the watermark. The quantization methods [7][8][9][10][16][17][18][19][20][21] are typical watermark embedding and extracting methods. It divides the reference axis into multiple index intervals, and each index interval corresponds to one component (0 or 1) of the watermark.…”

“…The watermarks can be embedded in either whole frames [3][4][5][6][7][8] or partial frames [9][10][11][12][13][14]. Obviously, the efficiency and the imperceptibility of the former are lower than that of the latter.…”

Compressive sensing based secret signals recovery for effective image Steganalysis in secure communications

“…Meanwhile, we use the support vector machine (SVM) to train all features to derive the best fitness function as it has demonstrated good performance in various applications [28] [33]. Figure 4 shows two statistical results of average feature extracted from four image databases in CS-DCT domain [38] and CS-SVM domain, respectively. Then we have applied our improved nature inspired CS algorithm and compared with optimized DPSO algorithm.…”