With the advancement of image acquisition devices and social networking services, a huge volume of image data is generated. Using different image and video processing applications, these image data are manipulated, and thus, original images get tampered. These tampered images are the prime source of spreading fake news, defaming the personalities and in some cases (when used as evidence) misleading the law bodies. Hence before relying totally on the image data, the authenticity of the image must be verified. Works of the literature are reported for the verification of the authenticity of an image based on noise inconsistency. However, these works suffer from limitations of confusion between edges and noise, post-processing operation for localization and need of prior knowledge about an image. To handle these limitations, a noise inconsistency-based technique has been presented here to detect and localize a false region in an image. This work consists of three major steps of pre-processing, noise estimation and post-processing. For the experimental purpose two, publicly available datasets are used. The result is discussed in terms of precision, recall, accuracy and f1-score on the pixel level. The result of the presented work is also compared with the recent state-of-the-art techniques. The average accuracy of the proposed work on datasets is 91.70%, which is highest among state-of-the-art techniques.

随着图像采集设备和社交网络服务的发展，产生了大量的图像数据。使用不同的图像和视频处理应用程序，这些图像数据被操纵，因此原始图像被篡改。这些篡改的图像是散布虚假新闻，诽谤人格，在某些情况下（当用作证据时）误导法律机构的主要来源。因此，在完全依赖图像数据之前，必须验证图像的真实性。据报道，文献的工作是基于噪声不一致性来验证图像的真实性。然而，这些工作受到以下限制：边缘和噪声之间的混淆，用于定位的后处理操作以及需要关于图像的先验知识。为了解决这些限制，这里提出了一种基于噪声不一致性的技术来检测和定位图像中的错误区域。这项工作包括预处理，噪声估计和后处理三个主要步骤。出于实验目的，使用了两个公开可用的数据集。在精度，召回率，准确性和像素级别的f1分数方面讨论了结果。提出的工作结果也与最新技术进行了比较。拟议的数据集工作的平均准确性为91.70％，在最新技术中最高。在精度，召回率，准确性和像素级别的f1分数方面讨论了结果。提出的工作结果也与最新技术进行了比较。拟议的数据集工作的平均准确性为91.70％，在最新技术中最高。在精度，召回率，准确性和像素级别的f1分数方面讨论了结果。提出的工作结果也与最新技术进行了比较。拟议的数据集工作的平均准确性为91.70％，在最新技术中最高。