A Survey On Deepfake Image Generation Using Sketch

Authors

  • Prof. K.N. Agalave  Department of Computer Engineering, S.B. Patil Collage of Engineering, Indapur, Maharashtra, India
  • Kudale Akanksha Narayan  Department of Computer Engineering, S.B. Patil Collage of Engineering, Indapur, Maharashtra, India
  • Modale Shreya Anil  Department of Computer Engineering, S.B. Patil Collage of Engineering, Indapur, Maharashtra, India
  • Tabe Pragati Shadanan  Department of Computer Engineering, S.B. Patil Collage of Engineering, Indapur, Maharashtra, India

Keywords:

Deep fake, Machine Learning, Generative AI, Deep Learning.

Abstract

The rapid advancement of deep fake technology presents unprecedented challenges to society, particularly in the realm of cybercrime and misinformation. Law enforcement agencies are faced with the growing threat of manipulated images, making it imperative to develop innovative solutions that can discern between authentic and fabricated visual content.

References

  1. Optimization of DeepFake Video Detection Using Image Preprocessing by Ali Berjawi, KhouloudSamrouth, Olivier Deforges (2023)
  2. CycleGAN: Unpaired Image-to-image Translation using Cycle-Consistent Adversarial Networks by Zhu et al.(2017)
  3. Progressive Growing of GANs for Improved Quality, Stability, and Variation by Karraset al.(2018)
  4. Gaikwad, Yogesh J. "A Review on Self Learning based Methods for Real World Single Image Super Resolution." (2021).
  5. V. Khetani, Y. Gandhi and R. R. Patil, "A Study on Different Sign Language Recognition Techniques," 2021 International Conference on Computing, Communication and Green Engineering (CCGE), Pune, India, 2021, pp. 1-4, doi: 10.1109/CCGE50943.2021.9776399.
  6. Vaddadi, S., Arnepalli, P. R., Thatikonda, R., & Padthe, A. (2022). Effective malware detection approach based on deep learning in Cyber-Physical Systems. International Journal of Computer Science and Information Technology, 14(6), 01-12.
  7. Thatikonda, R., Vaddadi, S.A., Arnepalli, P.R.R. et al. Securing biomedical databases based on fuzzy method through blockchain technology. Soft Comput (2023). https://doi.org/10.1007/s00500-023-08355-x
  8. Rashmi, R. Patil, et al. "Rdpc: Secure cloud storage with deduplication technique." 2020 fourth international conference on I-SMAC (IoT in social, mobile, analytics and cloud)(I-SMAC). IEEE, 2020.
  9. Khetani, V., Gandhi, Y., Bhattacharya, S., Ajani, S. N., & Limkar, S. (2023). Cross-Domain Analysis of ML and DL: Evaluating their Impact in Diverse Domains. International Journal of Intelligent Systems and Applications in Engineering, 11(7s), 253-262.
  10. Khetani, V., Nicholas, J., Bongirwar, A., & Yeole, A. (2014). Securing web accounts using graphical password authentication through watermarking. International Journal of Computer Trends and Technology, 9(6), 269-274.
  11. Kale, R., Shirkande, S. T., Pawar, R., Chitre, A., Deokate, S. T., Rajput, S. D., & Kumar, J. R. R. (2023). CR System with Efficient Spectrum Sensing and Optimized Handoff Latency to Get Best Quality of Service. International Journal of Intelligent Systems and Applications in Engineering, 11(10s), 829-839.
  12. Nagtilak, S., Rai, S., & Kale, R. (2020). Internet of things: A survey on distributed attack detection using deep learning approach. In Proceeding of International Conference on Computational Science and Applications: ICCSA 2019 (pp. 157-165). Springer Singapore.
  13. Mane, Deepak, and Aniket Hirve. "Study of various approaches in machine translation for Sanskrit language." International Journal of Advancements in Research & Technology 2.4 (2013): 383.
  14. Shivadekar, S., Kataria, B., Limkar, S. et al. Design of an efficient multimodal engine for preemption and post-treatment recommendations for skin diseases via a deep learning-based hybrid bioinspired process. Soft Comput (2023). https://doi.org/10.1007/s00500-023-08709-5
  15. Shivadekar, Samit, et al. "Deep Learning Based Image Classification of Lungs Radiography for Detecting COVID-19 using a Deep CNN and ResNet 50." International Journal of Intelligent Systems and Applications in Engineering 11.1s (2023): 241-250.
  16. StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-image Translation by Choi et al.(2018)
  17. SPADE: Semantic Image Synthesis with Spatially-Adaptive Normalization by Park et al.(2019)
  18. MUNIT: Multidimensional Unsupervised Image-toimage Translation by Huang et al.(2018)
  19. DeepFashion: Powering Robust Clothes Recognition and Retrievel with Rich Annotations by Liu et al.(2016)
  20. I2I-GAN: Image-to-image Translation vai Group-wise Deep Whitening and Coloring Transformation by Huang et al.(2018)
  21. CoGAN: Coupled Generative Adversarial Networks bu Liu et al.(2016)
  22. Image-to-image Translation with Conditional Adversarial Neworks (Pix2Pix) by Isola et al.(2016)

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Published

2023-10-30

Issue

Section

Research Articles

How to Cite

[1]
Prof. K.N. Agalave, Kudale Akanksha Narayan, Modale Shreya Anil, Tabe Pragati Shadanan, " A Survey On Deepfake Image Generation Using Sketch" International Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 9, Issue 10, pp.103-107, September-October-2023.