Hierarchical Categorization and Review of Recent Techniques on Image Forgery Detection

Vinolin, V.; Sucharitha, M.

doi:10.1093/comjnl/bxz148

Cited by 4 publications

(2 citation statements)

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“…The processing steps that an image goes through depend on the camera's model, and the other relevant features of the camera. The features of the device are often referred to as the device fingerprint, such as photo response non-uniformity noise pattern (Rani, 2019; Vinolin and Sucharitha, 2019). On being captured by the camera, the image goes into the memory, and then a series of processing steps are carried out, such as quantization, colour correlation, filtering, compression, gamma correction and white balancing (Gonzalez and Richard, 2018).…”

Section: Literature Reviewmentioning

confidence: 99%

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

Ghai

Kumar

Gupta

2021

ITP

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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.

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Section: Literature Reviewmentioning

confidence: 99%

“…Whenever an image has been tampered, it is uploaded in some manipulation software and resaved. This action introduces patterns in the image that can help to detect the forgery (Vinolin and Sucharitha, 2019). The blocking patterns are also altered when an image is manipulated.…”

Section: Literature Reviewmentioning

confidence: 99%