1. Yang Y, Zheng L, Zhang J, Cui Q, Li Z, Yu, PS (2018) TI-CNN: Convolutional Neural Networks for fake news detection

   Google Scholar 

2. Wu K, Yang S, Zhu KQ (2015) False rumors detection on sina weibo by propagation structures. In: 2015 IEEE 31st international conference on data engineering, pp 651–662

   Chapter  Google Scholar 

3. Ma J, Gao W, Mitra P, Kwon S, Jansen BJ, Wong KF, Cha M (2016) Detecting rumors from microblogs with recurrent neural networks

   Google Scholar 

4. Ma J, Gao W, Wong KF (2019) Detect rumors on twitter by promoting information campaigns with generative adversarial learning. In: The world wide web conference, pp 3049–3055

   Chapter  Google Scholar 

5. Jin Z, Cao J, Zhang Y, Zhou J, Tian Q (2016) Novel visual and statistical image features for microblogs news verification. IEEE Transactions on Multimedia 19(3):598–608

   Article  Google Scholar 

6. Boididou C, Andreadou K, Papadopoulos S, Dang-Nguyen DT, Boato G, Riegler M, Kompatsiaris Y (2015) Verifying multimedia use at MediaEval 2015. MediaEval 3(3):7

   Google Scholar 

7. Boididou C, Papadopoulos S, Dang-Nguyen D-T, Boato G, Riegler M, Middleton SE, Petlund A, Kompatsiaris Y et al (2016) Verifying multimedia use at MediaEval 2016. MediaEval

8. Jin Z, Cao J, Guo H, Zhang Y, Luo J (2017) Multi-modal fusion with recurrent neural networks for rumor detection on microblogs. In: Proceedings of the 25th ACM international conference on multimedia, pp 795–816

   Chapter  Google Scholar 

9. Qi P, Cao J, Yang T, Guo J, Li J (2019) Exploiting multi-domain visual information for fake news detection. In: 2019 IEEE International Conference on Data Mining (ICDM), pp 518–527

   Chapter  Google Scholar 

10. Chen T, Li X, Yin H, Zhang J (2018) Call attention to rumors: deep attention based recurrent neural networks for early rumor detection. In: Pacific-Asia conference on knowledge discovery and data mining, pp 40–52

    Google Scholar 

11. Liu Y, Wu YFB (2018) Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. Thirty-Second AAAI Conference on Artificial Intelligence

12. Wang Y, Ma F, Jin Z, Yuan Y, Xun G, Jha K et al (2018) Eann: event adversarial neural networks for multi-modal fake news detection. In: Proceedings of the 24th acm sigkdd international conference on knowledge discovery & data mining, pp 849–857

    Chapter  Google Scholar 

13. Khattar D, Goud JS, Gupta M, Varma V (2019) Mvae: multi-modal variational autoencoder for fake news detection. In: The world wide web conference, pp 2915–2921

    Chapter  Google Scholar 

14. Ajao O, Bhowmik D, Zargari S (2018) Fake news identification on twitter with hybrid cnn and rnn models. In: Proceedings of the 9th international conference on social media and society, pp 226–230

    Chapter  Google Scholar 

15. Jindal S, Sood R, Singh R, Vatsa M, Chakraborty T (2020) NewsBag: a multi-modal benchmark dataset for fake news detection

    Google Scholar 

16. Singhal S, Shah RR, Chakraborty T, Kumaraguru P, Satoh SI (2019) SpotFake: a multi-modal framework for fake news detection. In: 2019 IEEE fifth international conference on multimedia big data (BigMM), pp 39–47

    Chapter  Google Scholar 

17. Marra F, Gragnaniello D, Cozzolino D, Verdoliva L (2018) Detection of Gan-generated fake images over social networks. In: 2018 IEEE conference on multimedia information processing and retrieval (MIPR), pp 384–389

    Chapter  Google Scholar 

18. Sabir E, AbdAlmageed W, Wu Y, Natarajan P (2018) Deep multi-modal image-repurposing detection. In: Proceedings of the 26th ACM international conference on multimedia, pp 1337–1345

    Chapter  Google Scholar 

19. Pomari T, Ruppert G, Rezende E, Rocha A, Carvalho T (2018) Image splicing detection through illumination inconsistencies and deep learning. 2018 25th IEEE International Conference on Image Processing (ICIP):3788–3792

20. Maigrot C, Claveau V, Kijak E, Sicre R (2016) Mediaeval 2016: a multi-modal system for verifying multimedia use task

    Google Scholar 

21. Bayar B, Stamm MC (2016) A deep learning approach to universal image manipulation detection using a new convolutional layer. In: Proceedings of the 4th ACM workshop on information hiding and multimedia security, pp 5–10

    Chapter  Google Scholar 

22. Lago F, Phan QT, Boato G (2019) Visual and textual analysis for image trustworthiness assessment within online news. Security and Communication Networks

23. Cui L, Wang S, Lee D (2019) SAME: sentiment-aware multi-modal embedding for detecting fake news. In: Proceedings of the 2019 IEEE/ACM international conference on advances in social networks analysis and mining, pp 41–48

    Chapter  Google Scholar 

24. Volkova S, Ayton E, Arendt DL, Huang Z, Hutchinson B (2019) Explaining multi-modal deceptive news prediction models. In: Proceedings of the international AAAI conference on web and social media, vol 13, pp 659–662

    Google Scholar 

25. Tariq S, Lee S, Kim H, Shin Y, Woo SS (2018) Detecting both machine and human created fake face images in the wild. In: Proceedings of the 2nd international workshop on multimedia privacy and security, pp 81–87

    Chapter  Google Scholar 

26. Sabir E, Cheng J, Jaiswal A, AbdAlmageed W, Masi I, Natarajan P (2019) Recurrent convolutional strategies for face manipulation detection in videos. Interfaces (GUI) 3(1)

27. Güera D, Delp EJ (2018) Deepfake video detection using recurrent neural networks. In: 2018 15th IEEE international conference on advanced video and signal based surveillance (AVSS), pp 1–6

    Google Scholar 

28. Papadopoulou O, Zampoglou M, Papadopoulos S, Kompatsiaris Y (2017) Web video verification using contextual cues. In: Proceedings of the 2nd international workshop on multimedia forensics and security, pp 6–10

    Chapter  Google Scholar 

29. Zhou P, Han X, Morariu VI, Davis LS (2017) Two-stream neural networks for tampered face detection. In: 2017 IEEE conference on computer vision and pattern recognition workshops (CVPRW), pp 1831–1839

    Chapter  Google Scholar 

30. Rahmouni N, Nozick V, Yamagishi J, Echizen I (2017) Distinguishing computer graphics from natural images using convolution neural networks. In: 2017 IEEE workshop on information forensics and security (WIFS), pp 1–6

    Google Scholar 

31. He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 770–778

    Google Scholar 

32. Huang G, Liu Z, Van Der Maaten L, Weinberger KQ (2017) Densely connected convolutional networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4700–4708

    Google Scholar 

33. Chollet F (2017) Xception: deep learning with depthwise separable convolutions. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1251–1258

    Google Scholar 

34. Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D et al (2015) Going deeper with convolutions. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1–9

    Google Scholar 

35. Ferreira W, Vlachos A (2016) Emergent: a novel dataset for stance classification. In: Proceedings of the 2016 conference of the north American chapter of the association for computational linguistics: human language technologies, pp 1163–1168

    Google Scholar 

36. Amerini I, Ballan L, Caldelli R, Del Bimbo A, Serra G (2011) A sift-based forensic method for copy–move attack detection and transformation recovery. IEEE Transactions on Information Forensics and Security 6(3):1099–1110

    Article  Google Scholar 

37. Conforti C, Pilehvar MT, Collier N (2018) Towards automatic fake news detection: cross-level stance detection in news articles. In: Proceedings of the first workshop on fact extraction and VERification (FEVER), pp 40–49

    Chapter  Google Scholar 

38. Bourgonje P, Schneider JM, Rehm G (2017) From clickbait to fake news detection: an approach based on detecting the stance of headlines to articles. In: Proceedings of the 2017 EMNLP workshop: natural language processing meets journalism, pp 84–89

    Chapter  Google Scholar 

39. Thorne J, Chen M, Myrianthous G, Pu J, Wang X, Vlachos A (2017) Fake news stance detection using stacked ensemble of classifiers. In Proceedings of the 2017 EMNLP Workshop: Natural Language Processing meets Journalism, pp 80–83

40. Doegar A, Dutta M, Gaurav K (2019) CNN based image forgery detection using pre-trained AlexNet model. Int J Comput Intell & IoT 2(1)

41. Uliyan DM, Jalab HA, Wahab AWA (2015) Copy move image forgery detection using hessian and center symmetric local binary pattern. In: 2015 IEEE Conference on Open Systems (ICOS), pp 7–11

    Chapter  Google Scholar 

42. Uliyan DM, Jalab HA, Wahab AWA, Shivakumara P, Sadeghi S (2016) A novel forged blurred region detection system for image forensic applications. Expert Syst Appl 64:1–10

    Article  Google Scholar 

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