Deep Fake Face Detection

Deep Fake Face Detection

1Shlok Samund, 2Kure Jaideep , 3Shinde Sanket, 4Patel Anish, Prof.  5H. Agarawal

1,2,3,4 Undergrad. Student, Dept. of Information Technology & Science, Lonavala, Maharashtra

5Assistant Prof SKN Sinhgad Institute of Technology & Science, Lonavala, Maharashtra

Abstract –Deepfake technology has become a prominent tool for creating convincing forged media, posing significant threats to privacy, security, and trust in the digital age. This paper provides an overview of the current state of deepfake face detection methods and highlights the challenges and complexities associated with this rapidly evolving field.

The proliferation of deepfake videos and images has necessitated the development of robust and effective detection techniques. In this paper, we review the various approaches employed for deepfake face detection, which include traditional image analysis, machine learning, and deep learning algorithms. We discuss the strengths and limitations of these approaches, emphasizing the critical role of deep learning in achieving high detection accuracy.

Furthermore, we delve into the key challenges faced by researchers and practitioners in deepfake face detection. These challenges include the adaptability of deepfake generation methods, the emergence of more advanced generative models, and the need for large and diverse datasets for training and evaluation. We also explore ethical concerns and potential biases associated with deepfake detection systems.

Keywords: Deepfake, Deep Learning, Convolution neural network, Audio-Visual Analysis, Digital Trust, Data model.