Gharib Hager A, Abdelnapi Noha M M, Hosny Khalid M
Department of Computer Science, Faculty of Computers and Information, Suez University, P.O. BOX: 43221, Suez, Egypt.
Department of Information Technology, Faculty of Computers and Information, Zagazig University, P.O.BOX:44519, Zagazig, Egypt.
Sci Rep. 2025 Aug 7;15(1):28906. doi: 10.1038/s41598-025-09290-7.
Reliable zero-watermarking is a distortion-free approach to copyright protection, which has been a primary focus of digital watermarking research. Traditional zero-watermarking techniques often struggle to maintain resilience against geometric and signal processing attacks while ensuring high security and imperceptibility. Many existing methods fail to extract stable and distinguishable features, making them vulnerable to image distortions such as compression, filtering, and geometric transformations. This paper presents a robust zero-watermarking technique for color images, combining Local Binary Patterns (LBP) with deep features extracted from the CONV5-4 layer of the VGG19 neural network to overcome these limitations. Frequent domain transformations, utilizing the Discrete Wavelet Transform (DWT) and Discrete Cosine Transform (DCT), enhance feature representation and improve resilience. Furthermore, a chaotic encryption scheme based on the Lorenz system and the Logistic map is used to scramble the feature matrix and watermark, thereby ensuring increased security. The zero watermark is generated through an XOR operation, facilitating imperceptible and secure ownership verification. Experimental results show that the proposed method is highly resilient to various attacks, including scaling, noise, filtering, compression, and rotation. The extracted watermark maintains a low Bit Error Rate (BER) and a high Normalized Cross-Correlation (NCC). At the same time, the Peak Signal-to-Noise Ratio (PSNR) of attacked images remains optimal. Specifically, the BER values of the extracted watermarks were below 0.0022, and the NCC values were above 0.9959. In contrast, the average PSNR values of the attacked images reached 34.0692 dB, demonstrating the method's superior robustness and visual quality. Compared to existing zero-watermarking algorithms, the proposed method shows superior robustness and security, making it highly effective for multimedia copyright protection.
可靠的零水印是一种无失真的版权保护方法，一直是数字水印研究的主要焦点。传统的零水印技术在确保高安全性和不可感知性的同时，往往难以抵御几何和信号处理攻击。许多现有方法无法提取稳定且可区分的特征，使其容易受到诸如压缩、滤波和几何变换等图像失真的影响。本文提出了一种针对彩色图像的鲁棒零水印技术，将局部二值模式（LBP）与从VGG19神经网络的CONV5-4层提取的深度特征相结合，以克服这些限制。利用离散小波变换（DWT）和离散余弦变换（DCT）的频域变换增强了特征表示并提高了抗攻击能力。此外，基于洛伦兹系统和逻辑斯谛映射的混沌加密方案用于对特征矩阵和水印进行加扰，从而确保更高的安全性。通过异或运算生成零水印，便于进行不可感知且安全的所有权验证。实验结果表明，该方法对各种攻击具有高度抗性，包括缩放、噪声、滤波、压缩和旋转。提取的水印保持低误码率（BER）和高归一化互相关（NCC）。同时，受攻击图像的峰值信噪比（PSNR）保持最佳。具体而言，提取水印的BER值低于0.0022，NCC值高于0.9959。相比之下，受攻击图像的平均PSNR值达到34.0692dB，证明了该方法具有卓越的鲁棒性和视觉质量。与现有的零水印算法相比，该方法具有更高的鲁棒性和安全性，使其在多媒体版权保护方面非常有效。
