It has been demonstrated that this approach achieves a balance of resilience and invisibility.Īs previously stated, the findings are promising, but there are several drawbacks, including (1) high computational costs of embedding and extracting the watermark, (2) few methods designed for watermark identification, (3) low TPR, and (4) high FNR. presented an adaptive image watermarking technique based on a combination of singular value decomposition (SVD) and the Wang–Landau (WL). presented a robust image watermarking technique based on DWT, APDCBT, and SVD. proposed watermarking based on the combination of DWT, DCT, and SVD to improve robustness and invisibility. proposed the watermarking technique based on combination of the DWT and DCT to improve robustness and invisibility. , proposed watermarking based on the combination of DWT and SVD to improve robustness and invisibility. Sridhar 109 proposed watermarking based on a combination of DCT, DWT, and SVD to improve robustness and invisibility. proposed a hybrid watermarking technique based on DWT-SVD. introduced a simple watermarking system for maintaining medical photographs based on the DFT domain, which achieved good robustness and image quality. The watermarked image’s quality degradation was demonstrated to be modest. presented a simple watermarking based on the DFT-based with an optimal implementation radius. However, the watermarked image quality is unaffected. presented a watermarking based on the DCT transform, a differential evolution and kernel extreme learning machine (DE-KELM). , presented a watermarking based on the DCT domain to overcome the false positive detection problem. They are resistant to lossy compression as well as Gaussian noise. proposed a watermarking based on DWT and Haar wavelet. proposed watermarking based on DCT and repetition code. They maintain image quality but are not resistant to rotation and scaling.
presented a singular value decomposition (SVD)-based watermarking. They have good PSNR and improve security. proposed a watermarking based on DCT and fractal encoding. The techniques used in the hybrid domain are usually a mixture of spatial and transform-domain techniques. The watermark should then be inserted into the transform domain coefficients. In the transform domain, the method begins by converting and representing the original image in the frequency domain using a forward transform. As a result, significant bit (ISB) methods have been created to increase the watermarking system’s robustness and maintain its quality.
However, it is insufficiently robust to safeguard watermark data from many types of attacks. The LSB approach is the most well-known for watermarking based spatial domains. The approach in presents spatial image watermarking based on the widely used LSB substation technique. Image watermarking can also be accomplished with a variety of approaches, such as intermediate significant bits (ISB) or patchwork algorithms, as well as spread spectrum and correlation-based algorithms. Incorporating a watermark in the host image’s least significant bits (LSBs) is the simplest spatial domain image watermarking technique. These techniques are significantly simpler, more efficient, and faster to implement. In spatial domain, the technique adds invisible watermark data into pixel values of the host image. Watermarking techniques can be classed as watermarking based spatial, watermarking based transform, and watermarking based hybrid domain, according to domain. The proposed method has the advantages of being able to find images with the watermark in any database and not requiring a specific type or algorithm for embedding the watermark. This shows that the performance outcomes of the proposed approach are consistent. The findings reveal that our proposed method’s DA classifier with integrated features had the greatest TPR of 93.71 and the lowest FNR of 6.29. VOC2008 is a public database that we use. In order to assess the performance of the proposed method, we use a public database. The performance of the classifiers is evaluated using a variety of feature sets, and the best results are achieved. The DA and random forest use mean squared energy feature, mean amplitude feature, and combined feature vector as inputs for classification. As classifiers, discriminant analysis (DA) and random forests are used. As a result, this study advocated using the best Gabor features and classifiers to improve the accuracy of image watermarking identification. The major goal of the research is to find and identify photos that include a watermark, regardless of the method used to add the watermark or the shape of the watermark. Image watermarking is one of many methods for preventing unauthorized alterations to digital images.
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