Qazi Tanzeela, Shah Mohsin, Ali Mushtaq, Gul Faqir, Ahmad Muneer, Khan Ajmal
Department of Computer Science and Information Technology, Hazara University Mansehra, Pakistan.
Department of Computer Engineering, Gachon University, Seongnam-si, Gyeonggi-do, Republic of Korea.
PLoS One. 2025 Jul 17;20(7):e0327586. doi: 10.1371/journal.pone.0327586. eCollection 2025.
Copy move forgery is a type of image forgery in which a portion of the original image is copied and pasted in a new location on the same image. The consistent illumination and noise pattern make this kind of forgery more difficult to detect. In copy-move forgery detection, conventional approaches are generally effective at identifying simple multiple copy-move forgeries. However, the conventional approaches and deep learning approaches often fall short in detecting multiple forgeries when transformations are applied to the copied regions. Motivated from these findings, a transform domain method for generating and analyzing multiple copy-move forgeries is proposed in this paper. This method utilizes the discrete wavelet transform (DWT) to decompose the original and patch image into approximate (low frequency) and detail coefficients (high frequency). The patch image approximate and details coefficients are inserted into the corresponding positions of the original image wavelet coefficients. The inverse DWT (IDWT) reconstructs the processed image planes after modification which simulates the multiple copy move forgery. In addition, this approach is tested by resizing the region of interest with varying patch sizes resulting in an interesting set of outcomes when evaluated against existing state-of-the-art techniques. This evaluation allows us to identify gaps in existing approaches and suggest improvements for creating more robust detection techniques for multiple copy-move forgeries.
复制移动伪造是一种图像伪造类型，其中原始图像的一部分被复制并粘贴到同一图像的新位置。一致的光照和噪声模式使得这种伪造更难被检测到。在复制移动伪造检测中，传统方法通常在识别简单的多次复制移动伪造方面有效。然而，当对复制区域应用变换时，传统方法和深度学习方法在检测多次伪造时往往存在不足。基于这些发现，本文提出了一种用于生成和分析多次复制移动伪造的变换域方法。该方法利用离散小波变换（DWT）将原始图像和补丁图像分解为近似（低频）系数和细节系数（高频）。将补丁图像的近似系数和细节系数插入到原始图像小波系数的相应位置。逆离散小波变换（IDWT）在修改后重建处理后的图像平面，从而模拟多次复制移动伪造。此外，通过使用不同的补丁大小调整感兴趣区域的大小来测试该方法，在与现有最先进技术进行评估时会产生一组有趣的结果。这种评估使我们能够识别现有方法中的差距，并为创建更强大的多次复制移动伪造检测技术提出改进建议。
