Authors : Servepalli Moushmi Deekshith; Kandepu Niharika; Adapa Akanksha Sri Karthika; Gunji Deepika; Manoj Wadhwa

Volume/Issue : Volume 9 - 2024, Issue 4 - April

Google Scholar : https://tinyurl.com/4sepas8k

Scribd : https://tinyurl.com/2p8x6db6

DOI : https://doi.org/10.38124/ijisrt/IJISRT24APR1888

Abstract : One popular generative model with many uses is the Generative Adversarial Network (GAN). Based on this unique concept, recent investigations have shown that it is feasible to produce high-quality fake face photos. The misuse of those fictitious faces in picture manipulation might lead to moral, ethical, and legal issues. To identify fake face images produced by the best method available at the moment, we first propose a Convolutional Neural Network (CNN) based method in this paper [20]. We also present experimental evidence demonstrating that the proposed method can achieve satisfactory results with an average accuracy over 99.4%. To further bolster the logic of our approach, we also offer comparison findings based on a few variations of the suggested CNN design, such as the high pass filter, the quantity of layer groups, and the activation function.

Keywords : Fake Image Detection, Deep Learning, Convolutional Neural Networks, Generative Adversarial Networks (GAN).

References :

1. Elyassami S et al (2022) Fake news detection using ensemble learning and machine learning algorithms. Combating Fake News with Computational Intelligence Techniques. Springer, Cham, pp 149–162
2. Ozbay F.A, Alatas B. Fake news detection within online social media using supervised artificial intelligence algorithms.
3. Ahmad I., Yousaf M., Youaf S., Ahmad M.O. Fake news detection using machine learning esemble methods.
4. AlShariah, N.M., Khader, A., & Saudagar, J. Detecting Fake Images on Social Media using Machine Learning
5. Martín Abadi, Ashish Agarwal, Paul Barham, Eugene Brevdo, Zhifeng Chen, Craig Citro, Greg S Corrado, Andy Davis, Jeffrey Dean, Matthieu Devin, et al. 2016. Tensorflow: Large-scale machine learning on heterogeneous distributed systems. arXiv preprint arXiv:1603.04467 (2016).
6.  Belhassen Bayar and Matthew C Stamm. 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. 5–10.
7. David Berthelot, Tom Schumm, and Luke Metz. 2017. Began: Boundary equilibrium generative adversarial networks. arXiv preprint arXiv:1703.10717 (2017).
8. Gang Cao, Yao Zhao, Rongrong Ni, and Xuelong Li. 2014. Contrast enhancementbased forensics in digital images. IEEE transactions on information forensics and security 9, 3 (2014), 515–525.
9. Jiansheng Chen, Xiangui Kang, Ye Liu, and Z Jane Wang. 2015. Median filtering forensics based on convolutional neural networks. IEEE Signal Processing Letters 22, 11 (2015), 1849–1853.
10. Mo Chen, Vahid Sedighi, Mehdi Boroumand, and Jessica Fridrich. 2017. JPEGPhase-Aware Convolutional Neural Network for Steganalysis of JPEG Images. In ACM Workshop on Information Hiding and Multimedia Security. 75–84.
11. Hak-Yeol Choi, Han-Ul Jang, Dongkyu Kim, Jeongho Son, Seung-Min Mun, Sunghee Choi, and Heung-Kyu Lee. [n. d.]. Detecting composite image manipulation based on deep neural networks. In IEEE International Conference on Systems, Signals and Image Processing. 1–5.
12. Vincent Dumoulin, Jonathon Shlens, and Manjunath Kudlur. 2017. A learned representation for artistic style. In Proceedings of International Conference on Learning Representations.
13. Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. 2014. Generative adversarial nets. In Advances in neural information processing systems. 2672–2680.
14. Satoshi Iizuka, Edgar Simo-Serra, and Hiroshi Ishikawa. 2017. Globally and locally consistent image completion. ACM Transactions on Graphics 36, 4 (2017), 107:1–107:14.
15. Justin Johnson, Alexandre Alahi, and Li Fei-Fei. 2016. Perceptual losses for realtime style transfer and super-resolution. In European Conference on Computer Vision. Springer, 694–711.
16. Diederik P. Kingma and Jimmy Ba. 2014. Adam: A Method for Stochastic Optimization. CoRR abs/1412.6980 (2014). arXiv:1412.6980 http://arxiv.org/abs/1412.6980
17. Christian Ledig, Lucas Theis, Ferenc Huszar, Jose Caballero, Andrew Cunningham, Alejandro Acosta, Andrew Aitken, Alykhan Tejani, Johannes Totz, Zehan Wang, et al. 2017. Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 4681–4690.