A deep-learning-based image forgery detection framework for controlling the spread of misinformation

Abstract

PurposeWeb 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/approachThe 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.FindingsThe 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/implicationsThis 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 implicationsThis 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 implicationsIn 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/valueThis 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.

Efficient Approach towards Detection and Identification of Copy Move and Image Splicing Forgeries Using Mask R-CNN with MobileNet V1

“…In [ 46 ], the researchers presented a framework for classifying input images as authentic or forged by combining the image transformation techniques along with pretrained CNN. The three image transformation techniques such as LBP (local binary pattern), DWT (discrete wavelet transform), and ELA (error level analysis) were used to extract appropriate features.…”

Design of Automated Deep Learning-Based Fusion Model for Copy-Move Image Forgery Detection

“…Ghai et al [ 17 ] aim at designing a DL-based image counterfeit recognition architecture. The presented model focuses on detecting images counterfeit with splicing and copy-move methods.…”

Towards dissemination, detection and combating misinformation on social media: a literature review

“…As social media users are exposed to unverified information, which is difficult to authenticate (Apuke and Omar, 2021), there is a growing concern regarding the proliferation of the spread of misinformation on social media. It may take many forms, including deceptive text, photos, videos and other media (Ghai et al , 2021). Logical terminology such as “an expert has claimed”, “based on an experiment” and “following this logic” persuades people to accept biased or incorrect opinions (Goering et al , 2011).…”

“…However, these studies do not consider the misinformation related aspects of social media and how these aspects affect businesses. Moreover, there are several studies on social media misinformation, but they explicitly focus on health context while neglecting a broader perspective on business and industry (Fard and Verma, 2022;Ghai et al, 2021;Huang and Wang, 2020;Li et al, 2022;Schuetz et al, 2021;Talwar et al, 2019Talwar et al, , 2020. In addition, a comprehensive knowledge of reasons to disseminate misinformation and techniques to detect and combat misinformation is missing.…”