Video tampering localisation using features learned from authentic content

Johnston, Pamela; Elyan, Eyad; Jayne, Chrisina

doi:10.1007/s00521-019-04272-z

Cited by 24 publications

(10 citation statements)

References 29 publications

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“…Availability of annotated data is one major requirement in this approach, and localization is not addressed. Johnston et al [136] developed a framework using a CNN for tampering detection which extracted features from authentic content and utilized them to localize the tampered frames and regions. The CNN was trained to estimate quantization parameters, deblock setting and intra/inter mode of pixel patches from an H.264/AVC sequence with suitable accuracy.…”

Section: Methods Based On Deep Learningmentioning

confidence: 99%

Digital Video Tampering Detection and Localization: Review, Representations, Challenges and Algorithm

et al. 2022

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Digital videos are now low-cost, easy to capture and easy to share on social media due to the common feature of video recording in smart phones and digital devices. However, with the advancement of video editing tools, videos can be tampered (forged) easily for propaganda or to gain illegal advantages—ultimately, the authenticity of videos shared on social media cannot be taken for granted. Over the years, significant research has been devoted to developing new techniques for detecting different types of video tampering. In this paper, we offer a detailed review of existing passive video tampering detection techniques in a systematic way. The answers to research questions prepared for this study are also elaborated. The state-of-the-art research work is analyzed extensively, highlighting the pros and cons and commonly used datasets. Limitations of existing video forensic algorithms are discussed, and we conclude with research challenges and future directions.

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Section: Methods Based On Deep Learningmentioning

confidence: 99%

Digital Video Tampering Detection and Localization: Review, Representations, Challenges and Algorithm

et al. 2022

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“…Singh and Sharma [7] detected phony images on social media by using a customized CNN model with high-pass filters. A CNN model was presented by Johnston et al [8] to identify and locate tampered regions in edited films. To recognize and label the tampered regions in videos, the model used CNN to estimate a quantization parameter, intra/inter mode, and deblock setting of pixels patched up in videos.…”

Section: Related Workmentioning

confidence: 99%

Image Fake News Prediction Based on Random Forest and Gradient-boosting Methods

Saadi,

Al-Jawher

2023

J.port.sci.res.

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The internet technology of today makes it challenging to spread false information, particularly through photos, including fake news. In this study, fake news is identified and predicted using photos that have been altered or misrepresented. Effective detection systems are crucial because of the proliferation of false information that images might spread due to the use of image modification tools and social media. This paper provides a thorough analysis of fake news based on images. Among the main research areas are machine learning for classification models and image data embedding (feature extraction). Our novel methodology forecasts fake news in the form of altered or misleading photographs by using Random Forest and gradient-boosting algorithms to detect visual alterations such as picture editing and image synthesis. This research leverages massive image datasets from news channels and social media to train and assess predictive algorithms. Our results demonstrate that our method has strong recall and precision in identifying image-based fake news. We also discuss practical applications and real-time detection, such building tools to combat misinformation on social media and in news organizations. At 0.968 with 0.997, Gradient Boosting performs better than Random Forest.

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“…Singh and Sharma [ 31 ] used custom CNN model with high-pass filters for fake image detection over social platforms. Johnston et al [ 32 ] proposed a CNN model to spot and localize tampered regions in manipulated videos. The model used CNN to estimate a quantization parameter, intra/inter mode, and deblock setting of pixels patch up in videos to identify and mark the tampered regions in videos.…”

Section: Related Workmentioning

confidence: 99%

Predicting image credibility in fake news over social media using multi-modal approach

Singh

Sharma

2021

Neural Comput & Applic

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Social media are the main contributors to spreading fake images. Fake images are manipulated images altered through software or by other means to change the information they convey. Fake images propagated over microblogging platforms generate misrepresentation and stimulate polarization in the people. Detection of fake images shared over social platforms is extremely critical to mitigating its spread. Fake images are often associated with textual data. Hence, a multi-modal framework is employed utilizing visual and textual feature learning. However, few multi-modal frameworks are already proposed; they are further dependent on additional tasks to learn the correlation between modalities. In this paper, an efficient multi-modal approach is proposed, which detects fake images of microblogging platforms. No further additional subcomponents are required. The proposed framework utilizes explicit convolution neural network model EfficientNetB0 for images and sentence transformer for text analysis. The feature embedding from visual and text is passed through dense layers and later fused to predict fake images. To validate the effectiveness, the proposed model is tested upon a publicly available microblogging dataset, MediaEval (Twitter) and Weibo, where the accuracy prediction of 85.3% and 81.2% is observed, respectively. The model is also verified against the newly created latest Twitter dataset containing images based on India's significant events in 2020. The experimental results illustrate that the proposed model performs better than other state-of-art multi-modal frameworks.

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Video tampering localisation using features learned from authentic content

Cited by 24 publications

References 29 publications

Digital Video Tampering Detection and Localization: Review, Representations, Challenges and Algorithm

Digital Video Tampering Detection and Localization: Review, Representations, Challenges and Algorithm

Image Fake News Prediction Based on Random Forest and Gradient-boosting Methods

Predicting image credibility in fake news over social media using multi-modal approach

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