Abstract
With the intensive study of machine learning in digital watermarking, its ability to balance the robustness and transparency of watermarking technology has attracted researchers’ attention. Therefore, quantum genetic algorithm, which serves as an intelligent optimized scheme combined with biological genetic mechanism and quantum computing, is widely used in various fields. In this study, an adaptive robust blind watermarking algorithm by means of optimized quantum genetics (OQGA) and entropy classification-based SVM (support vector machine) is proposed. The host image was divided into two parts according to the odd and even rows of the host image. One part was transformed by DCT (discrete cosine transform), and then the embedding intensity and position were separately trained by entropy-based SVM and OQGA; the other part was by DWT (discrete wavelet transform), in which the key fusion was achieved by an ergodic matrix to embed the watermark. Simulation results indicate the proposed algorithm ensures the watermark scheme transparency as well as having better resistance to common attacks such as lossy JPEG compression, image darken, Gaussian low-pass filtering, contrast decreasing, salt-pepper noise, and geometric attacks such as rotation and cropping.
Highlights
A well-designed watermarking scheme shall have the following two characteristics: 1 the watermark is imperceptible in the carrier data; 2 the watermark is difficult to be destroyed by unauthorized parties
In the proposed watermarking scheme, the optimized quantum genetic algorithm is used as the main algorithm to train the embedding position in the DCT domain embedding watermarking process, and the ergodic matrix is used as an important element to generate the key to complete the fusion operation in the DWT domain embedding watermarking process
The peak signal-to-noise ratio (PSNR) and the normalized correlation coefficient (NC) were used to evaluate the similarity between the original image and the embedded watermark image. e robustness of the watermark scheme was tested after various attacks, while the imperceptibility of the watermark was evaluated by the PSNR value. e higher PSNR value indicates the better imperceptibility of the watermark. e higher the NC value indicates the better extraction effect of the watermark, which means the higher similarity between the extracted watermark and the original watermark
Summary
A well-designed watermarking scheme shall have the following two characteristics: 1 the watermark is imperceptible in the carrier data (transparency); 2 the watermark is difficult to be destroyed by unauthorized parties (robustness). If the quantum nongate mutations are executed in this case, the quantum bits will tend to change the state “0” to the state “π/2,” which results in the qubits being updated in the opposite direction, and it highly tends to cause population oscillations, losing the excellent information In response to this problem, Xu et al [16] proposed a quantum genetic algorithm based on Hadamard gate variation. Huan et al [17] proposed an improved quantum rotation gate adjustment scheme and introduced a limiting correction operator to make the quantum genetic algorithm have better global optimization ability. By analyzing the robust watermarking algorithms proposed by Zhou et al [18] and Kricha et al [19], we found that embedding watermark information in the DWT domain can effectively improve the anti-geometric attack ability for the watermark scheme by experiments.
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