Deepfake Detection System Using AI for Image, Audio, and Video

           The Deepfake Detection System Using AI for Image, Audio, and Video is a major step forward in the quest for the security of digital authenticity in the face of the rapidly evolving synthetic media technology space. The emergence of AI-based tools such as Open AI, generative models, deep learning frameworks, and platforms such as Google has made the creation of highly believable manipulated media content more accessible than ever before. Thus, the need for the Deepfake Detection System using AI is not only desirable but also essential. Conclusion The Deepfake Detection System using AI is not only a technological solution but also a vital security mechanism in the face of the rapidly evolving synthetic media technology space. Thus, the evolution of the Deepfake Detection System using AI is essential in the face of the rapidly evolving generative AI technology space, ensuring the evolution of AI is responsible and authentic, while also ensuring the integrity of AI-generated content.

[1]       Hanqing Zhao, Wenbo Zhou, Dongdong Chen, Tianyi Wei, Weiming Zhang, and Nenghai Yu, “Multi-Attentional Deepfake Detection,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021. [2]      Hao Lin, Weiqi Luo, Kangkang Wei, and Minglin Liu, “Improved Xception with Dual Attention Mechanism and Feature Fusion for Face Forgery Detection,” 2021.J. Ho et al., "Denoising Diffusion Probabilistic Models," NeurIPS, 2020. [3]      Sohail Ahmed Khan and Duc‑Tien Dang‑Nguyen, “Deepfake Detection: A Comparative Analysis,” arXiv preprint, 2023 Video deepfake detection using a hybrid CNN-LSTMTransformer model for identity verification (2025) – Though more focused on video (visual) and temporal modeling (CNN + LSTM + Transformer), it deals with video + identity verification, which is relevant for multimedia deepfake detection. [4]      Qinlin He, Chunlei Peng, Decheng Liu, Nannan Wang, and Xinbo Gao, “GazeForensics: Deepfake Detection via Gaze-Guided Spatial Inconsistency Learning,” Neural Networks Journal, 2024.   [5]      Ahmed Hatem Soudy, Omnia Sayed, and Salwa O. Slim, “Deepfake Detection Using Convolutional Vision Transformers and Convolutional Neural Networks,” Neural Computing and Applications, 2024.Multimodal Deepfake Detection (Sneha G et al., 2024) – A recent journal article proposing a system combining visual + auditory cues for deepfake detection of multimedia content. ijraset.com+1 [6]       Ashima Gajendra Singh and Pooja Sharma, “A Hybrid Deep Learning Framework for Robust Deepfake Detection Using CNN, LSTM, and Vision Transformers,” Journal of Electrical Systems, 2024.   [7]       Shereen Ahmed Hussien and Seif N. Mohamed, “DeepFake Video Detection Using Vision Transformer,” International Journal of Intelligent Computing and Information Sciences, 2024.   [8]      Yaning Zhang, Qiufu Li, Zitong Yu, and Linlin Shen, “Distilled Transformers with Locally Enhanced Global Representations for Face Forgery Detection,” 2024.