Purpose

Web users rely heavily on online content make decisions without assessing the veracity of the content. The online content comprising text, image, video or audio may be tampered with to influence public opinion. Since the consumers of online information (misinformation) tend to trust the content when the image(s) supplement the text, image manipulation software is increasingly being used to forge the images. To address the crucial problem of image manipulation, this study focusses on developing a deep-learning-based image forgery detection framework.

Design/methodology/approach

The proposed deep-learning-based framework aims to detect images forged using copy-move and splicing techniques. The image transformation technique aids the identification of relevant features for the network to train effectively. After that, the pre-trained customized convolutional neural network is used to train on the public benchmark datasets, and the performance is evaluated on the test dataset using various parameters.

Findings

The comparative analysis of image transformation techniques and experiments conducted on benchmark datasets from a variety of socio-cultural domains establishes the effectiveness and viability of the proposed framework. These findings affirm the potential applicability of proposed framework in real-time image forgery detection.

Research limitations/implications

This study bears implications for several important aspects of research on image forgery detection. First this research adds to recent discussion on feature extraction and learning for image forgery detection. While prior research on image forgery detection, hand-crafted the features, the proposed solution contributes to stream of literature that automatically learns the features and classify the images. Second, this research contributes to ongoing effort in curtailing the spread of misinformation using images. The extant literature on spread of misinformation has prominently focussed on textual data shared over social media platforms. The study addresses the call for greater emphasis on the development of robust image transformation techniques.

Practical implications

This study carries important practical implications for various domains such as forensic sciences, media and journalism where image data is increasingly being used to make inferences. The integration of image forgery detection tools can be helpful in determining the credibility of the article or post before it is shared over the Internet. The content shared over the Internet by the users has become an important component of news reporting. The framework proposed in this paper can be further extended and trained on more annotated real-world data so as to function as a tool for fact-checkers.

Social implications

In the current scenario wherein most of the image forgery detection studies attempt to assess whether the image is real or forged in an offline mode, it is crucial to identify any trending or potential forged image as early as possible. By learning from historical data, the proposed framework can aid in early prediction of forged images to detect the newly emerging forged images even before they occur. In summary, the proposed framework has a potential to mitigate physical spreading and psychological impact of forged images on social media.

Originality/value

This study focusses on copy-move and splicing techniques while integrating transfer learning concepts to classify forged images with high accuracy. The synergistic use of hitherto little explored image transformation techniques and customized convolutional neural network helps design a robust image forgery detection framework. Experiments and findings establish that the proposed framework accurately classifies forged images, thus mitigating the negative socio-cultural spread of misinformation.

中文翻译：

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一种基于深度学习的图像伪造检测框架，用于控制错误信息的传播

目的

网络用户严重依赖在线内容做出决定，而无需评估内容的真实性。包括文本、图像、视频或音频的在线内容可能被篡改以影响舆论。由于在线信息（错误信息）的消费者倾向于在图像补充文本时信任内容，因此越来越多地使用图像处理软件来伪造图像。为了解决图像处理的关键问题，本研究的重点是开发基于深度学习的图像伪造检测框架。

设计/方法/方法

提出的基于深度学习的框架旨在检测使用复制移动和拼接技术伪造的图像。图像转换技术有助于识别相关特征，以便网络进行有效训练。之后，使用预训练的定制卷积神经网络在公共基准数据集上进行训练，并使用各种参数在测试数据集上评估性能。

发现

对来自各种社会文化领域的基准数据集进行的图像转换技术和实验的比较分析确定了所提出框架的有效性和可行性。这些发现证实了所提出的框架在实时图像伪造检测中的潜在适用性。

研究限制/影响

这项研究对图像伪造检测研究的几个重要方面具有影响。首先，这项研究增加了最近关于图像伪造检测的特征提取和学习的讨论。虽然先前对图像伪造检测的研究手工制作了特征，但所提出的解决方案有助于自动学习特征并对图像进行分类的文献流。其次，这项研究有助于持续努力减少使用图像的错误信息的传播。现有关于错误信息传播的文献主要关注通过社交媒体平台共享的文本数据。该研究呼吁更加重视强大的图像转换技术的开发。

实际影响

这项研究对法医学、媒体和新闻等各个领域都有重要的实际意义，这些领域越来越多地使用图像数据进行推理。图像伪造检测工具的集成有助于在文章或帖子通过 Internet 共享之前确定其可信度。用户在互联网上共享的内容已成为新闻报道的重要组成部分。本文提出的框架可以进一步扩展和训练更多注释的现实世界数据，以便作为事实检查员的工具。

社会影响

在当前大多数图像伪造检测研究试图评估图像是真实的还是在离线模式下伪造的情况下，尽早识别任何趋势或潜在的伪造图像至关重要。通过从历史数据中学习，所提出的框架可以帮助对伪造图像进行早期预测，甚至在新出现的伪造图像发生之前就对其进行检测。总之，所提出的框架有可能减轻伪造图像在社交媒体上的物理传播和心理影响。

原创性/价值

本研究侧重于复制移动和拼接技术，同时整合迁移学习概念以高精度对伪造图像进行分类。迄今为止很少探索的图像转换技术和定制的卷积神经网络的协同使用有助于设计一个强大的图像伪造检测框架。实验和结果表明，所提出的框架准确地对伪造图像进行了分类，从而减轻了错误信息的负面社会文化传播。