As more and more office documents are captured, stored, and shared in digital format, and as image editing software are becoming increasingly more powerful, there is a growing concern about document authenticity. To prevent illicit activities, this paper presents a new method for detecting altered text in document images. The proposed method explores the relationship between positive and negative coefficients of DCT to extract the effect of distortions caused by tampering by fusing reconstructed images of respective positive and negative coefficients, which results in Positive-Negative DCT coefficients Fusion (PNDF). To take advantage of spatial information, we propose to fuse R, G, and B color channels of input images, which results in RGBF (RGB Fusion). Next, the same fusion operation is used for fusing PNDF and RGBF, which results in a fused image for the original input one. We compute a histogram to extract features from the fused image, which results in a feature vector. The feature vector is then fed to a deep neural network for classifying altered text images. The proposed method is tested on our own dataset and the standard datasets from the ICPR 2018 Fraud Contest, Altered Handwriting (AH), and faked IMEI number images. The results show that the proposed method is effective and the proposed method outperforms the existing methods irrespective of image type.

随着越来越多的办公文档以数字格式被捕获、存储和共享，并且随着图像编辑软件变得越来越强大，人们越来越关注文档的真实性。为了防止非法活动，本文提出了一种检测文档图像中更改文本的新方法。所提出的方法探索了DCT正负系数之间的关系，通过融合各自正负系数的重建图像来提取篡改引起的失真的影响，从而导致正负DCT系数融合（PNDF）。为了利用空间信息，我们建议融合输入图像的 R、G 和 B 颜色通道，从而产生 RGBF（RGB Fusion）。接下来，同样的融合操作用于融合PNDF和RGBF，这导致原始输入图像的融合图像。我们计算直方图以从融合图像中提取特征，从而产生特征向量。然后将特征向量馈送到深度神经网络，用于对更改后的文本图像进行分类。所提出的方法在我们自己的数据集和来自 ICPR 2018 欺诈竞赛、Altered Handwriting (AH) 和伪造 IMEI 号码图像的标准数据集上进行了测试。结果表明，所提出的方法是有效的，无论图像类型如何，所提出的方法都优于现有方法。所提出的方法在我们自己的数据集和来自 ICPR 2018 欺诈竞赛、Altered Handwriting (AH) 和伪造 IMEI 号码图像的标准数据集上进行了测试。结果表明，所提出的方法是有效的，无论图像类型如何，所提出的方法都优于现有方法。所提出的方法在我们自己的数据集和来自 ICPR 2018 欺诈竞赛、Altered Handwriting (AH) 和伪造 IMEI 号码图像的标准数据集上进行了测试。结果表明，所提出的方法是有效的，无论图像类型如何，所提出的方法都优于现有方法。