Image Fake News Detection using Efficient NetB0 Model
الموضوعات :
Yasmine Almsrahad
1
,
Nasrollah Moghaddam Charkari
2
1 - Department of Electrical and Computer Engineering, Tarbiat Modares University of Tehran, Tehran, Iran,
2 - Department of Electrical and Computer Engineering, Tarbiat Modares University of Tehran, Tehran, Iran,
الکلمات المفتاحية: Fake News, EfficientNet, Fake Image, Social Media, Error Level Analysis.,
ملخص المقالة :
Today, social networks have become a prominent source of news, significantly altering the way people obtain news from traditional media sources to social media. Alternatively, social media platforms have been plagued by unauthenticated and fake news in recent years. However, the rise of fake news on these platforms has become a challenging issue. Fake news dissemination, especially through visual content, poses a significant threat as people tend to share information in image format. Consequently, detecting and combating fake news has become crucial in the realm of social media. In this paper, we propose an approach to address the detection of fake image news. Our method incorporates the error level analysis (ELA) technique and the explicit convolutional neural network of the EfficientNet model. By converting the original image into an ELA image, it is possible to effectively highlight any manipulations or discrepancies within the image. The ELA image is further processed by the EfficientNet model, which captures distinctive features used to detect fake image news. Visual features extracted from the model are passed through a dense layer and a sigmoid function to predict the image type. To evaluate the efficacy of the proposed method, we conducted experiments using the CASIA 2.0 dataset, a widely adopted benchmark dataset for fake image detection. The experimental results demonstrate an accuracy rate of 96.11% for the CASIA dataset. The results outperform in terms of accuracy and computational efficiency, with a 6% increase in accuracy and a 5.2% improvement in the F-score compared with other similar methods.
[1] M. Celliers and M. Hattingh, “A Systematic Review on Fake News Themes Reported in Literature,” in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Springer, 2020, pp. 223–234. doi: 10.1007/978-3-030-45002-1_19.
[2] X. Zhang and A. A. Ghorbani, “An overview of online fake news: Characterization, detection, and discussion,” Inf Process Manag, vol. 57, no. 2, p. 102025, 2020, doi: 10.1016/j.ipm.2019.03.004.
[3] W. S. Paka, R. Bansal, A. Kaushik, S. Sengupta, and T. Chakraborty, “Cross-SEAN: A cross-stitch semi-supervised neural attention model for COVID-19 fake news detection,” Appl Soft Comput, vol. 107, p. 107393, 2021, doi: 10.1016/j.asoc.2021.107393.
[4] Y. Wang et al., “Weak supervision for fake news detection via reinforcement learning,” AAAI 2020 - 34th AAAI Conference on Artificial Intelligence, no. December 2019, pp. 516–523, 2020, doi: 10.1609/aaai.v34i01.5389.
[5] N. Guimarães, Á. Figueira, and L. Torgo, “An organized review of key factors for fake news detection,” pp. 1–10, 2021, [Online]. Available: http://arxiv.org/abs/2102.13433.
[6] S. Preston, A. Anderson, D. J. Robertson, M. P. Shephard, and N. Huhe, “Detecting fake news on Facebook: The role of emotional intelligence,” PLoS One, vol. 16, no. 3 March, pp. 1–13, 2021, doi: 10.1371/journal.pone.0246757.
[7] P. Meel and D. K. Vishwakarma, “Fake news, rumor, information pollution in social media and web: A contemporary survey of state-of-the-arts, challenges and opportunities,” Expert Syst Appl, vol. 153, p. 112986, 2020, doi: 10.1016/j.eswa.2019.112986.
[8] H. Allcott and M. Gentzkow, “Social media and fake news in the 2016 election,” Journal of Economic Perspectives, vol. 31, no. 2, pp. 211–236, 2017, doi: 10.1257/jep.31.2.211.
[9] S. Raza and C. Ding, “Fake news detection based on news content and social contexts: a transformer-based approach,” Int J Data Sci Anal, vol. 13, no. 4, pp. 335–362, May 2022, doi: 10.1007/s41060-021-00302-z.
[10] X. Zhang and A. A. Ghorbani, “An overview of online fake news: Characterization, detection, and discussion,” Inf Process Manag, vol. 57, no. 2, p. 102025, 2020, doi: 10.1016/j.ipm.2019.03.004.
[11] N. Hoy and T. Koulouri, “A Systematic Review on the Detection of Fake News Articles,” Oct. 2021, [Online]. Available: http://arxiv.org/abs/2110.11240.
[12] J. Jing, H. Wu, J. Sun, X. Fang, and H. Zhang, “Multimodal fake news detection via progressive fusion networks,” Inf Process Manag, vol. 60, no. 1, Jan. 2023, doi: 10.1016/j.ipm.2022.103120.
[13] A. Biswas, D. Bhattacharya, K. Anil Kumar, and A. Professor, “DeepFake Detection using 3D-Xception Net with Discrete Fourier Transformation,” Journal of Information Systems and Telecommunication (JIST) 3, no. 35. 2021. 161.
[14] S. Hangloo and B. Arora, “Combating multimodal fake news on social media: methods, datasets, and future perspective,” Multimed Syst, vol. 28, no. 6, pp. 2391–2422, Dec. 2022, doi: 10.1007/s00530-022-00966-y.
[15] D. K. Vishwakarma, D. Varshney, and A. Yadav, “Detection and veracity analysis of fake news via scrapping and authenticating the web search,” Cogn Syst Res, vol. 58, pp. 217–229, Dec. 2019, doi: 10.1016/j.cogsys.2019.07.004.
[16] D. Mangal and Di. K. Sharma, “Fake News Detection with Integration of Embedded Text Cues and Image Features,” ICRITO 2020 - IEEE 8th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions), pp. 68–72, 2020, doi: 10.1109/ICRITO48877.2020.9197817.
[17] A. Mahmoodzadeh, “Human Activity Recognition based on Deep Belief Network Classifier and Combination of Local and Global Features,” .” J. Inf. Syst. Telecommun 9, 2021.
[18] Z. Jin, J. Cao, Y. Zhang, J. Zhou, and Q. Tian, “Novel Visual and Statistical Image Features for Microblogs News Verification,” IEEE Trans Multimedia, vol. 19, no. 3, pp. 598–608, 2017, doi: 10.1109/TMM.2016.2617078.
[19] F. Marra, D. Gragnaniello, D. Cozzolino, and L. Verdoliva, “Detection of GAN-Generated Fake Images over Social Networks,” Proceedings - IEEE 1st Conference on Multimedia Information Processing and Retrieval, MIPR 2018, pp. 384–389, 2018, doi: 10.1109/MIPR.2018.00084.
[20] B. Singh and D. K. Sharma, “SiteForge: Detecting and localizing forged images on microblogging platforms using deep convolutional neural network,” Comput Ind Eng, vol. 162, no. October, 2021, doi: 10.1016/j.cie.2021.107733.
[21] J. Xue, Y. Wang, S. Xu, L. Shi, L. Wei, and H. Song, MVFNN: Multi-Vision Fusion Neural Network for Fake News Picture Detection, vol. 1300, no. 2018. Springer International Publishing, 2020. doi: 10.1007/978-3-030-63426-1_12.
[22] C. Boididou et al., “Verifying multimedia use at MediaEval 2015,” CEUR Workshop Proc, vol. 1436, no. September, 2015.
[23] I. B. K. Sudiatmika, F. Rahman, Trisno, and Suyoto, “Image forgery detection using error level analysis and deep learning,” Telkomnika (Telecommunication Computing Electronics and Control), vol. 17, no. 2, pp. 653–659, 2019, doi: 10.12928/TELKOMNIKA.V17I2.8976.
[24] N. Krawetz, “A Picture’s Worth... Digital Image Analysis and Forensics,” 2007. [Online]. Available: www.hackerfactor.com.
[25] Paganini and Pierluigi, "Photo forensics: Detect Photoshop manipulation with error level analysis." Chief Information Security Officer at Bit4Id, 2013.
[26] H. Farid, “Exposing Digital Forgeries from JPEG Ghosts,” IEEE transactions on information forensics and security 154-160, 4.1 .2009.
[27] M. Tan and Q. V. Le, “EfficientNet: Rethinking model scaling for convolutional neural networks,” 36th International Conference on Machine Learning, ICML 2019, vol. 2019-June, pp. 10691–10700, 2019.
[28] W. and T. T. Jing Dong, “CASIA IMAGE TAMPERING DETECTION EVALUATION DATABASE Jing Dong , Wei Wang and Tieniu Tan Institute of Automation , Chinese Academy of Sciences,” pp. 422–426, 2013.
[29] D. Khattar, M. Gupta, J. S. Goud, and V. Varma, “MvaE: Multimodal variational autoencoder for fake news detection,” The Web Conference 2019 - Proceedings of the World Wide Web Conference, WWW 2019, pp. 2915–2921, 2019, doi: 10.1145/3308558.3313552.
[30] P. Qi, J. Cao, T. Yang, J. Guo, and J. Li, “Exploiting multi-domain visual information for fake news detection,” Proceedings - IEEE International Conference on Data Mining, ICDM, vol. 2019-Novem, no. Icdm, pp. 518–527, 2019, doi: 10.1109/ICDM.2019.00062.
[31] F. Yu, Q. Liu, S. Wu, L. Wang, and T. Tan, “A Convolutional Approach for Misinformation Identification,” 2017. [Online]. Available: http://www.npr.org/2016/11/08/500686320/did-social-media-
