With the present-day rapid evolution of digital technology, images have become one of the most important means of communication and information carrier in our society. Since the last decade, with the emergence of social networking sites like Facebook, Instagram, Twitter, etc., there has been a huge increase in the amount of information exchanged in the form of digital images, on a regular basis. While traditionally we might have had faith in the credibility of these images, today’s digital technology has begun to erode that faith. Before sharing an image over social networks, editing it with relevant software application has become one of the simplest things to do today. While not many people do this with any sinister intent behind, there has been a significant increase in cybercrimes related to malicious image manipulation and sharing. To this end, image splicing has emerged as one of the major forms of image manipulation attacks, among others today. This demands investigation of intrinsic differences between authentic and forged images and hence development of automated splicing detection tools. Here, we propose a blind image splicing detection technique that employs a deep convolutional residual network architecture as a backbone, followed by a fully connected classifier network, that classifies between authentic and spliced images. The classifier networks have been evaluated using the CASIA v2.0 dataset. Both are proven to yield accuracies more than 96% on an average, having surpassed the state-of-the-art results.

随着当今数字技术的迅速发展，图像已成为我们社会中最重要的通信和信息载体之一。自最近十年以来，随着诸如Facebook，Instagram，Twitter等社交网站的兴起，定期以数字图像形式交换的信息量已大大增加。传统上我们可能对这些图像的可信度有信心，但如今的数字技术已开始侵蚀这种信念。在社交网络上共享图像之前，使用相关的软件应用程序对其进行编辑已成为当今最简单的事情之一。尽管这样做的人并不多，其背后有任何险恶的意图，但与恶意图像操纵和共享有关的网络犯罪已大大增加。为此，图像拼接已成为当今图像处理攻击的主要形式之一。这要求调查真实图像和伪造图像之间的内在差异，并因此需要开发自动拼接检测工具。在这里，我们提出了一种盲图像拼接检测技术，该技术采用深度卷积残差网络体系结构作为主干，然后是一个完全连接的分类器网络，可以在真实图像和拼接图像之间进行分类。分类器网络已使用CASIA v2.0数据集进行了评估。事实证明，两者均超过了最新结果，平均产生的准确度超过96％。这要求调查真实图像和伪造图像之间的内在差异，并因此需要开发自动拼接检测工具。在这里，我们提出了一种盲图像拼接检测技术，该技术采用深度卷积残差网络体系结构作为主干，然后是一个完全连接的分类器网络，可以在真实图像和拼接图像之间进行分类。分类器网络已使用CASIA v2.0数据集进行了评估。事实证明，两者均超过了最新结果，平均产生的准确度超过96％。这要求调查真实图像和伪造图像之间的内在差异，并因此需要开发自动拼接检测工具。在这里，我们提出了一种盲图像拼接检测技术，该技术采用深度卷积残差网络体系结构作为主干，然后是一个完全连接的分类器网络，可以在真实图像和拼接图像之间进行分类。分类器网络已使用CASIA v2.0数据集进行了评估。事实证明，两者均超过了最新结果，平均产生的准确度超过96％。在真实图像和拼接图像之间进行分类。分类器网络已使用CASIA v2.0数据集进行了评估。事实证明，两者均超过了最新结果，平均产生的准确度超过96％。在真实图像和拼接图像之间进行分类。分类器网络已使用CASIA v2.0数据集进行了评估。事实证明，两者均超过了最新结果，平均产生的准确度超过96％。