Abstract
Social media have earned fame for consuming the news due to their quick proliferation and availability. However, they are also the main contributor in spreading fake news. Fake images spread over microblogging platforms like Twitter generates misrepresentation and arouses destructive emotions in consumers. This makes the detection of fake images over social platforms an extremely critical challenge. Deep learning methods learn the latent features of images and can be utilized in detecting fake images. In this paper, we have used the ResNet-50 a residual neural network to detect fake images. The images are passed through the Error Level Analysis process before being input to the deep learning model to subside the image's main features and bloat the latent features of manipulation. The model is verified against the Twitter image dataset. The experiment proves that residual neural networks perform well in detecting fake images over social media platforms.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Stoll J (2020) Reading fake news about the Coronavirus in Norway 2020, by source. https://www.statista.com/statistics/1108710/reading-fake-news-about-the-coronavirus-in-norway-by-source/. Accessed May 2 2020
Boididou C, Papadopoulos S, Dang-Nguyen D-T, Boato G, Riegler M, Middleton SE, Petlund A, Kompatsiaris Y (2016) Verifying Multimedia Use at MediaEval 2016
Sharma S, Sharma DK (2019) Fake news detection: a long way to go. In: 4th international conference on information systems and computer networks (ISCON), pp 816–821
Li G, Wu Q, Tu D, Sun S (2007) A sorted neighborhood approach for detecting duplicated regions in image forgeries based on DWT and SVD. In: Proceedings of IEEE international conference on multimedia and expo (ICME‘07), IEEE, Beijing, China, pp 1750–1753
Mahmood T, Nawaz T, Irtaza A, Ashraf R, Shah M, Mahmood MT (2016) Copy-move forgery detection technique for forensic analysis in digital images. Hindawi Publishing Corporation Mathematical Problems in Engineering 8713202, 13
Jwaid MF, Baraskar TN (2017) Study and analysis of copy-move & splicing image forgery detection techniques. In: International conference on I-SMAC (IoT in social, mobile, analytics, and cloud) (I-SMAC), pp 697–702
Alamro L, Nooraini Y (2017) Copy-move forgery detection using integrated DWT and SURF. J Telecommun, Electron Comput Eng (JTEC), pp 67–71
Hussain M, Qasem S, Bebis G, Muhammad G, Aboalsamh H, Mathkour H (2015) Evaluation of image forgery detection using multiscale weber local descriptors. Int J Artif Intell Tools 24(4):1540016
Chen B, Yu M, Su Q, Shim HJ, Shi Y (2018) Fractional Quaternion Zernike moments for robust color image copy-move forgery detection. IEEE Access, 56637–56646
Popescu AC, Farid H (2005) Exposing digital forgeries in color filter array interpolated images. IEEE Trans Signal Process, 3948–3959
Ferrara P, Bianchi T, Rosa AD, Piva A (2012) Image forgery localization via fine-grained analysis of CFA artifacts. IEEE Trans Inf Forensics Secur 7(5):1566–1577
Sheng H, Shen X, Lyu Y, Shi Z, Ma S (2018) Image splicing detection based on Markov features in discrete octonion cosine transform domain. IET Image Proc 12(10):1815–1823
Mazumdar A, Bora PK (2016) Exposing splicing forgeries in digital images through dichromatic plane histogram discrepancies. In: Proceedings of the Tenth Indian conference on computer vision, graphics and image processing, 62, pp 1–8
Jaiswal AK, Srivastava R (2020) A technique for image splicing detection using hybrid feature set. Multimed Tools (2020), 11837–11860
Rao Y, Ni J (2016) A deep learning approach to detection of splicing and copy-move forgeries in images. In: IEEE international workshop on information forensics and security
Bayar B, Stamminger MC (2016) A deep learning approach to universal image manipulation detection using a new convolutional layer. In: IH&MMSec, Proceedings of the 4th ACM workshop on information hiding and multimedia security, pp 5–10
Rehman YAU, Po LM, Liu M (2018) LiveNet: Improving features generalization for face liveness detection using convolution neural networks. Expert Syst Appl 108:159–169
Liu B, Pun C-M (2020) Exposing splicing forgery in realistic scenes using deep fusion network. Inf Sci 2020(526):133–150
Xiao B, Wei Y, Bi X, Li W, Ma J (2020) Image splicing forgery detection combining coarse to refined convolutional neural network and adaptive clustering. Inf Sci 511:172–219
Jin Z, Cao J, Guo H, Zhang Y, Luo J (2017) Multimodal fusion with recurrent neural networks for rumor detection on microblogs. In: ACM on multimedia conference, p 795
Mangal D, Sharma DK (2020) Fake news detection with integration of embedded text cues and image features. In: 8th international conference on reliability, Infocom technologies and optimization (trends and future directions) (ICRITO), pp 68–72
Krawetz N (2007) A picture’s worth… Hacker Factor Solutions. https://www.hackerfactor.com/papers. Accessed May 10 2020
Robertson A (2012) During hurricane sandy, misinformation and fact-checking clash on Twitter. https://www.theverge.com/2012/10/30/3577778/hurricane-sandy-twitter-instagram-misinformation
Dwivedi P Understanding and coding a ResNet in Keras. https://towardsdatascience.com/understanding-and-coding-a-resnet-in-keras-446d7ff84d33
Durfee C (2019) A gentle introduction to cross-entropy for machine learning. https://www.aiproblog.com/index.php/2019/10/20/a-gentle-introduction-to-cross-entropy-for-machine-learning/. Accessed July 42020
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Singh, B., Sharma, D.K. (2022). Detecting Image Forgery Over Social Media Using Residual Neural Network. In: Sanyal, G., Travieso-González, C.M., Awasthi, S., Pinto, C.M., Purushothama, B.R. (eds) International Conference on Artificial Intelligence and Sustainable Engineering. Lecture Notes in Electrical Engineering, vol 837. Springer, Singapore. https://doi.org/10.1007/978-981-16-8546-0_32
Download citation
DOI: https://doi.org/10.1007/978-981-16-8546-0_32
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-16-8545-3
Online ISBN: 978-981-16-8546-0
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)