Adaptive Robust Blind Watermarking Scheme Improved by Entropy‐Based SVM and Optimized Quantum Genetic Algorithm

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.

A Novel Comprehensive Watermarking Scheme for Color Images

“…Earlier digital watermarking technologies [1,2] focused on grayscale images, and watermarks were embedded in spatial or frequency domains. With the development of artificial intelligence and the special demand for host images, adaptive watermarking [3,4], reversible watermarking [5], and deep learning watermarking [6] have received attention. In recent years, watermarking has been required to achieve higher robustness, and researchers expect more purposes are packed in a watermarking scheme; thus, it promotes the development of multipurpose watermarking.…”

“…(2) Step 2: use the layer R and the layer G to generate hash sequence. (3) Step 3: embed the watermark sequence in the layer B using the LSB embedding method[20] (4). Step 4: repeat steps 2-3 until all the blocks are processed.ALGORITHM 3: Embedding fragile watermark.…”

“…Step 2: use the layer R and the layer G to generate the hash sequence. (3) Step 3: extract the stored watermark sequence from the layer B using the LSB algorithm and compared with the generated sequence by step 2[20] (4). Step 4: repeat steps 2-3 until the full picture is traversed.…”

An entropy-based image watermarking scheme by improving robustness in shearlet and wavelet domain

“…Zhou et al [36] and Zhang et al [37] apply ergodic matrix in the image encryption scheme and obtain a good result. It is demonstrated that an ergodic matrix with proper parameters [36,37] can be employed to completely shuffle the original image and has an immense key space of at least3.08×10 5898 . Therefore, the same method is used in watermark preprocessing in our research.…”

“…Therefore, the same method is used in watermark preprocessing in our research. For preprocessing is not our main concern, interested readers can refer to [36,37] .…”