• Mrs.R.Jayalakshmi
• S.Harikumaran
• A.Anthoni Stepan Raj
• M.Aakash
• B.Naresh Krishnan

In response to concerns over the efficacy of existing face forgery detection methods, researchers have introduced a novel solution by integrating a 3D Convolutional Neural Network (3DCNN) algorithm into the detection framework. Unlike traditional methods, 3DCNN considers the temporal dimension, enabling it to capture spatiotemporal features in data, thereby enhancing prediction accuracy. This integration addresses limitations of previous approaches, particularly in scenarios involving subtle or dynamic manipulations in fake faces. By analyzing volumetric data across spatial and temporal domains, the updated system achieves improved sensitivity to intricate details, resulting in a more reliable face forgery detection mechanism. This advancement represents a significant step in staying ahead of evolving forgery techniques, emphasizing the importance of continually refining detection methodologies to combat potential misuse of face manipulation technology.

3D Convolutional Neural Network (3DCNN), wavelet dual-branch network,2D convolutional networks, face forgery

"ENHANCED FACE FORGERY DETECTION USING AI", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.5, Issue 7, page no.396-402, July-2018, Available :http://www.jetir.org/papers/JETIR1807A58.pdf

"ENHANCED FACE FORGERY DETECTION USING AI", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.5, Issue 7, page no. pp396-402, July-2018, Available at : http://www.jetir.org/papers/JETIR1807A58.pdf