Papers by Bhuvanesh Singh
International Conference on Computing for Sustainable Global Development, Mar 17, 2021
Social media platforms play a significant role in spreading news in the current digital era. Howe... more Social media platforms play a significant role in spreading news in the current digital era. However, they have also been spreading fake images. Forged images posted on social media platform such as Twitter create misrepresentation and generate harmful user emotions. Thus, detecting fake images over social media platforms has become a critical need of time. Deep learning convolutional networks can learn the intrinsic feature set of images and can detect forged images. This paper proposes a convolutional neural network to spot fake images shared over social media platforms. High pass filters from image processing are used in the first layer for weight initialization. This helps the neural network converge faster and achieve better accuracy. Interpretability is a common concern in deep learning models. The proposed framework employs Gradient-weighted Class Activation Mapping to generate heatmaps and localize the image's manipulated area. The model is verified against the publicly available CASIA dataset. An accuracy of 92.3% is achieved, which is better than the other previous models. From the social media perspective, the model is verified against the latest Twitter dataset. The experiment proves that convolutional neural networks perform well in detecting forged images over social media platforms, and interpretability can be achieved.
2022 9th International Conference on Computing for Sustainable Global Development (INDIACom), Mar 23, 2022
Computers & Industrial Engineering, Dec 1, 2021
Electronics, Feb 13, 2023
This article is an open access article distributed under the terms and conditions of the Creative... more This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY
2023 6th International Conference on Information Systems and Computer Networks (ISCON)
Neural Computing and Applications, 2021
Social media are the main contributors to spreading fake images. Fake images are manipulated imag... more 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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Papers by Bhuvanesh Singh