Lakshmi C, Nithya C, Sivaraman R, Sridevi A, Santhiyadevi R, Ravichandran Dhivya, Banu S Aashiq, Mahalingam Hemalatha, Meikandan Padmapriya Velupillai, Amirtharajan Rengarajan
School of Electrical & Electronics Engineering, SASTRA Deemed University, Thanjavur, 613 401, India.
School of Computing, SASTRA Deemed University, Thanjavur, 613 401, India.
Sci Rep. 2025 Aug 26;15(1):31460. doi: 10.1038/s41598-025-17059-1.
Digital e-governance has grown tremendously due to the massive information technology revolution. Banking, Healthcare, and Insurance are some sectors that rely on ownership identification during various stages of service provision. Watermarking has been employed as a primary factor in authenticating stakeholders in such circumstances. In this work, a three-layer feature-dependent image watermarking approach in the transform domain has been proposed. In this Discrete Wavelet Transform (DWT) influenced approach, the first decomposition level holds the Singular Values of a specific encrypted logo. In the second level of decomposition, the specific textual authentication signature is included in an arithmetic coding tag. The third level of decomposition has been utilised to keep the concerned identity of the owner in a compressed form using run-length coding. The proposed uniqueness of the scheme involves embedding a heavy payload watermark in the chosen grayscale cover image by utilising feature extraction and data compression, with a focus on preserving perceptual transparency and robustness. Various geometric variations and noise patterns, including Gaussian, salt and pepper, rotation, and cropping, were applied to the watermarked image to ensure its attack-resistant capability. After extracting the watermarks one by one, the reversibility of the cover image has been recovered through the Convolutional Neural Network (CNN) with a very low Mean Square Error (MSE). The proposed DWT-based scheme has achieved high perceptual transparency, as demonstrated by the Structural Similarity Index Measure (SSIM) and Normalised Correlation (NC) approach, which approaches unity. The integration of CNN enhances its robustness by recovering images against various attacks, as evidenced by achieving a PSNR of about 44 dB. Additionally, high SSIM (> 0.99) and NC (~ 1.0) values indicate enhanced perceptual transparency and robustness. The Bit error rate remained minimal post-attack, confirming reliable watermark recovery with CNN-aided restoration.
由于大规模的信息技术革命，数字电子政务得到了极大的发展。银行、医疗保健和保险等行业在服务提供的各个阶段都依赖于所有权识别。在这种情况下，水印已被用作验证利益相关者的主要因素。在这项工作中，提出了一种变换域中基于三层特征的图像水印方法。在这种受离散小波变换（DWT）影响的方法中，第一分解层保存特定加密标志的奇异值。在第二分解层，特定的文本认证签名包含在算术编码标签中。第三分解层已被用于使用行程编码以压缩形式保存所有者的相关身份。该方案的独特之处在于通过利用特征提取和数据压缩，在选定的灰度覆盖图像中嵌入高容量水印，重点是保持感知透明度和鲁棒性。对水印图像应用了各种几何变换和噪声模式，包括高斯噪声、椒盐噪声、旋转和裁剪，以确保其抗攻击能力。在逐一提取水印后，通过卷积神经网络（CNN）以非常低的均方误差（MSE）恢复了覆盖图像的可逆性。所提出的基于DWT的方案具有很高的感知透明度，如结构相似性指数测量（SSIM）和归一化相关性（NC）方法所示，其接近1。CNN的集成通过针对各种攻击恢复图像来增强其鲁棒性，实现约44 dB的峰值信噪比（PSNR）证明了这一点。此外，高SSIM（>0.99）和NC（~1.0）值表明感知透明度和鲁棒性得到增强。攻击后误码率保持极低，证实了通过CNN辅助恢复实现可靠的水印恢复。
