An intermediate significant bit (ISB) watermarking technique using neural networks

Abstract

Prior research studies have shown that the peak signal to noise ratio (PSNR) is the most frequent watermarked image quality metric that is used for determining the levels of strength and weakness of watermarking algorithms. Conversely, normalised cross correlation (NCC) is the most common metric used after attacks were applied to a watermarked image to verify the strength of the algorithm used. Many researchers have used these approaches to evaluate their algorithms. These strategies have been used for a long time, however, which unfortunately limits the value of PSNR and NCC in reflecting the strength and weakness of the watermarking algorithms. This paper considers this issue to determine the threshold values of these two parameters in reflecting the amount of strength and weakness of the watermarking algorithms. We used our novel watermarking technique for embedding four watermarks in intermediate significant bits (ISB) of six image files one-by-one through replacing the image pixels with new pixels and, at the same time, keeping the new pixels very close to the original pixels. This approach gains an improved robustness based on the PSNR and NCC values that were gathered. A neural network model was built that uses the image quality metrics (PSNR and NCC) values obtained from the watermarking of six grey-scale images that use ISB as the desired output and that are trained for each watermarked image’s PSNR and NCC. The neural network predicts the watermarked image’s PSNR together with NCC after the attacks when a portion of the output of the same or different types of image quality metrics (PSNR and NCC) are obtained. The results indicate that the NCC metric fluctuates before the PSNR values deteriorate.

Digital Image Watermarking Techniques: A Review

“…The watermark pixel value is tested, according to the range of each bit-plane, and then the original image file pixel is placed outside any of the edges of the range [68]. There are eight (08) bit planes in grayscale images, where the first bit-plane holds the MSB while the eighth contains the LSB, and the remaining (second-seventh) bit-planes are used as ISB [69]. If the pixel value of a grayscale image is 133 (10,000,101), then intermediate significant bits are represented by Figure 10.…”

“…In this way, the watermark can be embedded by measuring the first-row elements in the matrix R. The proposed method has better robustness against the addition of noise, image compression, cropping, and filtering, as compared to related existing methods. For copyright protection, a new algorithm was studied in the DWT domain [69]. This blind scheme embeds the logo watermark directly into the three-level DWT decomposed sub-bands.…”

“…A bit-plane of a digital image.PSNR and NCC are the most frequently used quality metrics for watermarked images, where PSNR determines the intensities of strength and weakness of watermarking techniques, while the latter validates the strength of the used algorithm applied to a watermarked image after attacks. Considering this issue, the paper[69] revealed the amount of strength and weakness of digital image watermarking techniques by defining the threshold values of PSNR and NCC. The discussed watermarking technique embedded four watermarks to the ISB of six grayscale image files one-byone by substituting the original image pixels with new pixels and keeping them close to the original pixel values simultaneously.…”

Common Gabor Features for Image Watermarking Identification

“…Incorporating a watermark in the host image's least significant bits (LSBs) [5] is the simplest spatial domain image watermarking technique. Image watermarking can also be accomplished with a variety of approaches, such as intermediate significant bits (ISB) [6] or patchwork algorithms, as well as spread spectrum and correlation-based algorithms. The approach in [7] presents spatial image watermarking based on the widely used LSB substation technique.…”

Detection of Suspicious Terrorist Emails Using Text Classification: A Review

“…Many terrorists use e-mail as a means to communicate [10,11]. After the 9/11 incident, many researchers have developed new approaches [12][13][14] and software tools [15,16] to analyze and mine terrorism-related information to prevent terrorism-related activities. Such research has provided beneficial contributions to the succeeding generation of counterterrorism tools.…”