• Dr.Uttara Gogate
• Hariom Haushilaprasad Singh
• Ritik Ramchandra Prajapati
• Sahil Rajendra Patil

Advancements in artificial intelligence and machine learning have made it easier to manipulate digital media, leading to the emergence of deepfake technology. Deepfakes refer to synthetic media, such as images and videos that have been altered or created using AI algorithms. These manipulated visual media can be used to spread misinformation, defame individuals, or deceive the public. This project aims to combat the negative implications of deepfakes by developing a website that can accurately detect and identify such manipulated content. The existing systems provides less accurately detect deepfakes. To overcome the limitations of existing system Convolutional Neural Networks (CNN) is used which provides high accuracy. In this project four types of deepfake i.e. FaceSwap, Face Attribute Manipulation, Face Reenactment and Lip-sync detection are detected successfully with high accuracy.

Deepfake generation and detection, Misinformation, Generative Adversarial Networks (GAN), Face Attribute Manipulation, Deepfake research, Face Reenactment.

"Face Integrity: Comprehensive Facial Authenticity Assurance", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 7, page no.40-47, July-2024, Available :http://www.jetir.org/papers/JETIRGK06005.pdf

"Face Integrity: Comprehensive Facial Authenticity Assurance", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 7, page no. pp40-47, July-2024, Available at : http://www.jetir.org/papers/JETIRGK06005.pdf