• Dr.Jayasudha K
• Harshitha B S
• Nanditha N
• Utsahi Shubha M G Hiremath
• Varsha R

This study explores advanced deep learning methodologies for detecting deep fake content in video and audio, addressing the pressing need for robust solutions against sophisticated forgeries. Deep fake technologies leverage neural networks to create realistic fake videos and synthetic audio, posing significant threats to information integrity and privacy. This work examines convolutional neural networks (CNNs), recurrent neural networks (RNNs), and attention mechanisms for identifying visual and temporal anomalies in video deep fake, such as inconsistencies in facial textures, movements, and lip synchronization. Additionally, audio detection methods focus on spectrogram analysis and Mel-Frequency Cepstral Coefficients (MFCCs) using CNNs and transformers to detect unnatural frequency patterns and temporal rhythms in synthetic speech. Despite the advancements in detection, challenges remain due to the rapidly evolving sophistication of deep fake algorithms and the need for computationally efficient, real-time solutions. The research highlights the potential of multi-modal detection systems that simultaneously analyze video and audio features, aiming to enhance detection accuracy and resilience against adversarial attacks.

Deep Learning, Face-Forensic++, Convolutional Neural Network (CNN), Recurrent neural networks (RNNs), Deep fake detection challenge, Celeb-DF

"Deep Fake Video and Audio Detection Using Deep Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 12, page no.c796-c804, December-2024, Available :http://www.jetir.org/papers/JETIR2412292.pdf

"Deep Fake Video and Audio Detection Using Deep Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 12, page no. ppc796-c804, December-2024, Available at : http://www.jetir.org/papers/JETIR2412292.pdf