Digital watermarking is a significant issue in the field of information security and avoiding the misuse of images in the world of Internet and communication. This paper proposes a novel watermarking method for tamper detection and recovery using semi-fragile data hiding, based on lifting wavelet transform (LWT) and feed-forward neural network (FNN). In this work, first, the host image is decomposed up to one level using LWT, and the discrete cosine transform (DCT) is applied to each 2×2 blocks of diagonal details. Next, a random binary sequence is embedded in each block as the watermark by correlating DC coefficients. In the authentication stage, first, the geometry is analyzed by using speeded up robust features (SURF) algorithm and extract watermark bits by using FNN. Afterward, logical exclusive or operation between original and extracted watermark is applied to detect tampered region. Eventually, in the recovery stage, tampered regions are recovered using the inverse halftoning technique. The performance and efficiency of the method and its robustness against various geometric, non-geometric, and hybrid attacks are reported. From the experimental results, it can be seen that the proposed method is superior in terms of robustness and quality of the watermarked and recovered images, respectively, compared to the state-of-the-art methods. Besides, imperceptibility has been improved by using different correlation steps as the gain factor for flat (smooth) and texture (rough) blocks. Based on the advantages exhibited, the proposed method outperforms the related works, in terms of superiority, efficiency, and effectiveness for tamper detection and recovery-based applications.

中文翻译：

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基于提升小波变换和前馈神经网络的有效半脆弱水印图像认证方法

数字水印是信息安全领域中的一个重要问题，如何避免在互联网和通信领域滥用图像。本文提出了一种基于提升小波变换（LWT）和前馈神经网络（FNN）的，利用半脆弱数据隐藏进行篡改检测和恢复的水印新方法。在这项工作中，首先，使用LWT将主机图像分解到一个级别，然后将离散余弦变换（DCT）应用于每个2×2对角线细节块。接下来，通过使DC系数相关，在每个块中嵌入随机二进制序列作为水印。在身份验证阶段，首先，使用加速鲁棒特征（SURF）算法分析几何形状，并使用FNN提取水印位。之后，在原始水印和提取出的水印之间进行逻辑异或运算，以检测篡改区域。最终，在恢复阶段，使用反半色调技术恢复篡改的区域。报告了该方法的性能和效率，以及其针对各种几何，非几何和混合攻击的鲁棒性。从实验结果可以看出，与最新技术相比，该方法在水印和恢复图像的鲁棒性和质量方面均更为出色。此外，通过使用不同的相关步骤作为平坦（平滑）块和纹理（粗糙）块的增益因子，提高了感知能力。基于所展现的优势，该方法在优势，效率，