Anti Spoofing And Deepfake Detection

The rapid growth of artificial intelligence has enabled the creation of highly realistic deepfake videos, creating significant challenges for digital security, information authenticity, and public trust. To address this issue, this research proposed a deep learning–based deepfake detection framework that combines a ResNeXt Convolutional Neural Network (CNN) for spatial feature extraction with a Long Short-Term Memory (LSTM) network for temporal sequence analysis. The system processes video data through preprocessing, face extraction, feature learning, and classification stages to accurately distinguish between authentic and manipulated videos. Experimental results demonstrate that the proposed approach effectively detects visual artifacts and temporal inconsistencies commonly found in deepfake content while maintaining reliable performance across diverse video samples. The developed framework provides an automated, efficient, and scalable solution for multimedia authentication, digital forensics, and cybersecurity applications. It can assist in reducing the spread of misinformation, identity fraud, and other malicious activities involving manipulated media. In the future, the system can be enhanced by incorporating advanced architectures such as Vision Transformers (ViT), EfficientNet, and multimodal learning techniques that analyze visual, audio, and behavioral cues simultaneously. Further improvements may include real-time detection of live video streams, deployment on mobile and web platforms, integration with social media monitoring systems, and training on larger, more diverse datasets to improve robustness against emerging deepfake generation techniques. These advancements will contribute to building more secure and trustworthy digital media environments.

[1] A. Rössler, D. Cozzolino, L. Verdoliva, C. Riess, J. Thies, and M. Nießner, “FaceForensics++: Learning to Detect Manipulated Facial Images,” Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2019. [2] B. Dolhansky, J. Bitton, B. Pflaum, J. Lu, R. Howes, M. Wang, and C. C. Ferrer, “The Deepfake Detection Challenge (DFDC) Dataset,” arXiv preprint arXiv:2006.07397, 2020. [3] Y. Li, X. Yang, P. Sun, H. Qi, and S. Lyu, “Celeb-DF: A Large-Scale Challenging Dataset for DeepFake Forensics,” Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020. [4] I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, “Generative Adversarial Nets,” Advances in Neural Information Processing Systems (NeurIPS), 2014. [5] K. He, X. Zhang, S. Ren, and J. Sun, “Deep Residual Learning for Image Recognition,” Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016. [6] S. Xie, R. Girshick, P. Dollár, Z. Tu, and K. He, “Aggregated Residual Transformations for Deep Neural Networks,” Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017. [7] S. Hochreiter and J. Schmidhuber, “Long Short-Term Memory,” Neural Computation, vol. 9, no. 8, pp. 1735–1780, 1997. [8] A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan, et al., “PyTorch: An Imperative Style, High-Performance Deep Learning Library,” Advances in Neural Information Processing Systems (NeurIPS), 2019. [9] G. Bradski, “The OpenCV Library,” Dr. Dobb’s Journal of Software Tools, 2000. [10] Y. LeCun, Y. Bengio, and G. Hinton, “Deep Learning,” Nature, vol. 521, no. 7553, pp. 436–444, 2015.