DEEP FAKE VIDEO DETECTION

Registration ID: IJNRD_225795

Published ID: IJNRD2408046

DOI: Click Here to Get

P.Priya nandini , Dr.M.Vikram

Deepfake,DenseNet,AI

With the advancement of artificial intelligence (AI) and cloud computing, audio, video, and image manipulation techniques have grown faster and more sophisticated. This type of media content is known as deepfakes. It is becoming increasingly possible for computers to control media in increasingly convincing ways, for instance, by having a duplicate of a public individual's voice or superimposing one person's face onto another. Media confirmation, media provenance, and deepfake discovery are the three types of countermeasures used to counteract deepfakes. Multi-modal detection techniques are used in deepfake detection solutions to detect whether target media has been altered or synthesized. There are two types of detection techniques in use today: manual and algorithmic. Techniques utilizing human media analysts with access to software include traditional manual techniques. AI-based algorithms are used in algorithmic detection to detect manipulated media. We aim to build a Deep Learning Binary Classifier to help detect deepfake videos which can be applied to solve several real-world problems caused by deepfakes ranging from distortion of democratic discourse; manipulation of elections; Weakening the credibility of institutions; weakening journalism; causing social divisions; sabotaging public well-being; and causing long-lasting damage to prominent people, including chose authorities and possibility for office. In this work, we present our profound convolutional neural organization put together model approved with respect to Deepfake Detection Challenge dataset for crucial AI research in deepfake discovery. Here we train the dataset on various convolution neural network models likeInception_Resnet_v2,DenseNetandcustommodels. OurbestmodelofInception_Resnet_v2 has an accuracy score of 97.73%. Byimplementing this project, we hope to automate the detection of deep- fake videos. The model has demonstrated that it is possible to use these types of models and apply it in real world for curbing deepfake videos.

Published Paper   E-Certificate

P.Priya nandini & Dr.M.Vikram (August-2024). DEEP FAKE VIDEO DETECTION. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 9(8), a499-a509. https://ijnrd.org/papers/IJNRD2408046.pdf

Volume 9 Issue 8, August-2024

Pages : a499-a509

Paper Reg. ID: IJNRD_225795

Published Paper Id: IJNRD2408046

Downloads: 000122001

Research Area: Computer Engineering 

Country: ysr , AP, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2408046.pdf

Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2408046

Journal Name: INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT(IJNRD)

ISSN: 2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar | ESTD YEAR: 2016

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