Deep Fake and Image Manipulation
Keywords:
Generative Adversarial Networks (GAN), Computer Graphics, Watermarking, Deepfake, Artificial Intelligence, Detection TechniquesAbstract
It has become more difficult to detect the difference between real and false media because to the rapid advancements in computer graphics and highly artificial intelligence in the production of realistic photos and videos in recent years. complicated. Even if these computer-generated images or movies have practical uses, they can also present privacy and security risks. Deepfake is one method that carries these hazards. Combining the terms "deep learning" and "fake" yields the term "deepfake." Anyone can use Deepfake to edit or erase another person's face from a photo or video. The speech and facial emotions of an original image or video can likewise be altered using deepfakes. These days, deepfake algorithms use artificial intelligence and deep learning to replace the original voice, face, or emotions. It's difficult to tell whether the content has been modified by deepfake techniques. Deepfakes are altered and retouched using deep learning algorithms, which makes it more difficult to distinguish between actual and fake images. Generative adversarial neural networks (GANs) are used to create deepfakes, which may be dangerous for the public. It's imperative to spot fake graphic content with great attention. Several investigations have been carried out to identify deepfakes in photo manipulation. The two main problems with the existing approaches are their time consumption and accuracy.
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References
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Frid-Adar, M., Diamant, I., Goldberger, J., & Greenspan, H. (2018). "GAN-based Synthetic Medical Image Augmentation for increased CNN Performance in Liver Lesion Classification." Neurocomputing, 321, 321-331.
Attias, D., Michaeli, T., & Irani, M. (2019). "Logical Adversarial Networks for Active Visual Testing." In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV).
Matern, F., Riess, C., Stotzka, R., et al. (2019). "Exploiting Visual Artifacts to Expose Deepfakes and Face Manipulations." In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV).
Rossler, A., Cozzolino, D., Verdoliva, L., et al. (2019). "FaceForensics++: Learning to Detect Manipulated Facial Images." In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV).
Li, Y., Yang, X., Sun, P., et al. (2018). "Exposing DeepFake Videos by Detecting Face Warping Artifacts." In arXiv preprint arXiv:1811.00656.
Marra, A., Gragnaniello, D., & Verdoliva, L. (2019). "Detection of GAN-Generated Fake Images Over Social Networks." In Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
Hsu, C., Wu, W., & Kirchherr, J. (2020). "Deep Learning-Based Image Forensics: A Comprehensive Review." IEEE Access, 8, 187760-187782.
Cozzolino, D., Gragnaniello, D., & Verdoliva, L. (2018). "Sparsity-Based Forensic Analysis of Deep Learning Models." In Proceedings of the European Conference on Computer Vision (ECCV).
Nguyen, A., Yosinski, J., & Clune, J. (2015). "Deep Neural Networks are Easily Fooled: High Confidence Predictions for Unrecognizable Images." In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
Bayar, B., & Stamm, M. C. (2016). "A Deep Learning Approach to Universal Image Manipulation Detection Using a New Convolutional Layer." In Proceedings of the IEEE Workshop on Information Forensics and Security (WIFS).
Afchar, D., Nozick, V., Yamagishi, J., & Echizen, I. (2018). "MesoNet: A Compact Facial Video Forgery Detection Network." In Proceedings of the ACM Workshop on Information Hiding and Multimedia Security (IH&MMSec).
Zhou, Y., Ye, Q., Qiu, W., et al. (2017). "Two-Stream Inception Network for Detection of Faked Images and Videos." In Proceedings of the IEEE International Workshop on Information Forensics and Security (WIFS).
Güera, D., Erdenebat, M., & Erdenebayar, U. (2019). "Deepfake Video Detection Using Recurrent Neural Networks." In Proceedings of the IEEE International Conference on Image Processing (ICIP).
Tolosana, R., Vera-Rodriguez, R., Fierrez, J., et al. (2020). "DeepFakes and Beyond: A Survey of Face Manipulation and Fake Detection." Information Fusion, 64, 131-148.
Zhao, Y., Zheng, L., Zheng, Z., et al. (2020). "Detecting Deepfake Videos from the Clues Left in the Deep Learning Models." In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
Frid-Adar, M., Diamant, I., Goldberger, J., & Greenspan, H. (2018). "GAN-based Synthetic Medical Image Augmentation for increased CNN Performance in Liver Lesion Classification." Neurocomputing, 321, 321-331.
Attias, D., Michaeli, T., & Irani, M. (2019). "Logical Adversarial Networks for Active Visual Testing." In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV).
Matern, F., Riess, C., Stotzka, R., et al. (2019). "Exploiting Visual Artifacts to Expose Deepfakes and Face Manipulations." In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV).
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