目的 图像水印技术通过在图像中嵌入标识版权的水印信息来实现版权保护。其中，局部水印技术将水印嵌入特定图像区域，可防止水印被裁剪攻击破坏，同时尽量减小视觉影响。该技术通常利用特征点进行局部区域的定位和同步。然而，水印嵌入及后续可能的图像攻击容易引起特征点偏移，导致无法准确定位嵌入区域，造成水印提取失败。因此，提高特征点稳定性对局部水印技术的可靠性至关重要。方法 本文提出感知约束和引导下的特征点增强局部水印算法，通过自适应修改图像像素一次，同时实现水印嵌入和特征点增强两种操作，达到增强特征点稳定性、提高水印鲁棒性和保证水印不可感知性三种效果。算法的自适应性体现在两个方面：一是使用优化函数寻找最佳像素修改方案，在嵌入水印的同时增强特征点强度，避免水印嵌入削弱特征点稳定性，增强其抗攻击能力，提高水印的鲁棒性；二是，水印嵌入过程中的像素修改总量由峰值信噪比约束，并根据感知引导模型实现各像素修改量的差异化分配，最大限度地确保水印的不可感知性。结果 实验结果证明，本文所提算法对特征点的稳定性有显著增强，在嵌入水印图像的峰值信噪比高于40dB的前提下，水印提取的准确率在大多数攻击的情况下都优于目前先进局部水印算法。结论 本文所提的算法有效提高了特征点的稳定性，在水印不可见性和水印鲁棒性方面均获得了更优的效果。

Objective The technology of image watermarking plays a vital and indispensable role in the realm of copyright protection by embedding unique identifiers within digital images. This technological advancement enables content owners to assert and verify ownership, as well as trace unauthorized use of their intellectual property. Local image watermarking technology embeds the watermark into specific regions of an image, which helps to prevent the watermark from being compromised by cropping attacks while minimizing visual distortion as much as possible. As a result, local image watermarking technology, compared to global watermarking technology, ensures the robustness and integrity of the watermark while maintaining the visual quality of the original image. The localization and synchronization of embedding region in local image watermarking are typically facilitated by feature point, which serve as reference marker for embedding the watermark and are critical for ensuring accurate extraction during watermark detection processes. The feature point provides a consistent and reliable framework for embedding the watermark, thereby enhancing the precision and effectiveness of the watermarking technique. However, watermark embedding and potential image attacks can easily cause the displacement of feature point, resulting in inaccurate localization of the embedded region. This displacement can consequently lead to failures in watermark extraction, compromising the effectiveness of the watermarking process. To address these challenges, it is imperative to enhance the stability of feature point in local watermarking technologies. Stability refers to the resilience of feature point against various image distortions or attacks that could potentially alter its intended position. This stability directly impacts the effectiveness of watermark embedding and subsequent extraction processes, thereby influencing the overall robustness of the copyright protection mechanism. A stable feature point ensures that the watermark remains accurately embedded and can be reliably extracted, even in the face of adversarial conditions. Method This paper proposes CoEE(Concurrent watermark embedding and feature point enhancement with perceptual constraint and guidance), a method that performs both watermark embedding and feature point enhancement by adaptively modifying pixels once. It can achieve three significant effects: improved stability of feature point, enhanced robustness, and ensured imperceptibility to maintain the visual quality of the image. The adaptability of the algorithm is underscored by two key aspects. Firstly, an optimization function is designed to get the optimal pixel modification strategy that enhances the strength of feature point and embeds the watermark simultaneously. This strategy involves a careful analysis and adjustment of pixel values to ensure that the feature points maintain their stability and resilience even after the watermark is embedded. By doing so, it prevents the weakening of feature point stability that typically occurs during the watermark embedding process, thereby improving the system's resistance to various forms of attacks. This enhanced resistance ensures that the watermark remains robust and detectable under adverse conditions, thereby providing a reliable means of copyright protection. Secondly, the total amount of pixel modifications during the watermark embedding process is constrained by the Peak Signal-to-Noise Ratio, which serves as a quantitative measure of the changes allowed in the image to maintain its visual quality. To go a step further, the total amount of pixel modifications is allocated to individual pixels under perceptual guidance, taking the human visual system's sensitivity to changes in different parts of the image into account. The allocation strategy aims to maximize the imperceptibility of the embedded watermark, ensuring that the modifications are distributed in such a manner that they remain largely unnoticed by the human eye. By adhering to the PSNR constraints, the strategy guarantees that the watermarked image maintains its high visual quality. This careful balance between robustness and imperceptibility ensures that the watermark is invisible to viewers, while still being robust enough to be detected and accurately extracted, thereby achieving a seamless integration of copyright protection within the digital content. Result The experiments demonstrate that CoEE algorithm significantly enhances the stability of feature points. When subjected to various attack scenarios, the accuracy of watermark extraction using the CoEE algorithm significantly surpasses the performance of the current state-of-the-art watermarking algorithms. This superior performance is consistently observed provided that the Peak Signal-to-Noise Ratio (PSNR) of the watermarked image remains above the threshold of 40dB. Consequently, the CoEE algorithm demonstrates a notable improvement in watermark extraction accuracy and resilience under diverse attack conditions. Conclusion The algorithm proposed in this paper significantly enhances the stability of feature points, resulting in superior performance in both watermark invisibility and watermark robustness.