## 1 Introduction
### Image Watermarking Backgrounds  
Watermark-based methods for detecting image forgery can be divided into two categories: spatial domain [26] and frequency domain [27; 28]. Spatial domain methods involve directly inserting a watermark into an image's pixel values. Among these techniques, the LSB (least significant bit) method is used to preserve image quality by attempting to insert the watermark into the least significant bit. This method replaces a pixel's rightmost bit value with a watermark bit. Another spatial domain method is the MSB (most significant bit) method, which divides the image into non-overlapping squares and calculates the local contrast between the pixel in the center of the block and its surroundings. This method is primarily used for semi-fragile watermarks and is more reliable than LSB methods for general image modifications. The histogram modification method inserts a watermark by shifting a value between a histogram's minimum and maximum values. However, this method has a lower insertion ability than other methods. Spatial domain methods outperform frequency domain methods in terms of speed and embeddable watermark ability. Watermark techniques that use the frequency domain involve converting an image into various frequency domains, such as DCT (discrete cosine transform), DWT (discrete wavelet transform), and DFT (discrete Fourier transform). These techniques do not directly embed a watermark into the pixel value of the image but rather embed it into the conversion coefficient value, which is then used to create a watermarked image. The use of frequency domains in watermarking can produce higher-quality results because it can more easily account for the characteristics of the human visual system than methods that use spatial domains. [PERSON] and [PERSON] [11] used a Gaussian mixture model to determine the distribution parameters of wavelet coefficients, which represent the statistical characteristics of wavelet regions in images. Their algorithm divides the wavelet region into \(N\times N\) blocks, extracts parameters from one watermarking block and the remainder of the reference block, and inserts a watermark. However, because the referenced blocks are so large, this algorithm cannot localize the counterfeit area if the image is modulated. Another study by [PERSON] [12] uses wavelet packet decomposition of images and the owner's keys to select coefficients and levels for watermarking. This algorithm establishes block dependence using keys and takesadvantage of HVS (human visual system) characteristics to reduce distortion, although the watermarked image distortion is greater than that of other tasks. [PERSON]'s approach [13] involves watermarking only the coefficients of high-frequency sub-bands at the most accurate scale of the luminance component to minimize embedding distortion and maintain high counterfeit detection and localization accuracy. The algorithm does not involve or watermark any other coefficients or components during the insertion process of embeddable coefficients. However, the algorithm has a limitation in that it represents dependence on a block basis during the insertion process, which makes blocks susceptible to collage attacks that are well-known as soft watermark attacks because they are not interdependent. In a previous study, a watermark-based technique was utilized to authenticate the original JPEG-1 image [29]. In this paper, we examined watermark-based methods for integrity authentication of JPEG 2000 images. The watermark embedding process resulted in a watermarked image that was visually indistinguishable from the original. The contributions of this paper are summarized as follows:  
1. We design our scheme to fulfill all the requirements specified by JPEG Privacy and Security [4]. 2. In JPEG 2000 coding, images are encoded in the DWT domain. Our proposed algorithms embed the watermark in the LSB plane of the DWT coefficients.