Leveraging the Deep Fake Voice and Image for Robust Forgery Detection using Machine Learning

Registration ID: IJNRD_214608

Published ID: IJNRD2403231

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Suganya P , Gokul P , Prasath R , Sathya Ranjan Sahoo S

recurrent neural network (RNN), convolutional neural networks (CNNs), deep learning, Deepfake

Multimediaforensicshasmaderemarkablestrides in the detection of manipulations within multimedia content driven by deep learning techniques. Despite these advancements, a major impediment has been the scarcity of comprehensive datasets necessary for effectively training convolutionalneural networks (CNNs), which arecommonly used in multimedia forensics. Researchers have proposed a strategic solution to this challenge by advocating for the integration of recurrent neural network (RNN) algorithms. Unlike CNNs, RNNs are well-suited for handling sequential data and capturing temporal dependencies, addressing the limitations posed by the static nature of CNNs. This integration is poised to usher in a new era by significantly enhancing prediction accuracy in multimedia forensics. The significance of integrating RNNs becomes particularlyevident in the context of assessing the authenticity of multimedia objects, especially when deep learning techniques have been employed for manipulation. The temporal dynamics and sequential patterns inherent in RNNs make them adept at discerningsubtlealterationsinmultimediacontentovertime, thus offering a more nuanced and accurate analysis. This capability is crucial in the face of evolving digital manipulations where adversaries continually refine their techniques.TheintegrationofRNNsintomultimediaforensic toolsrepresentsapromisingavenueforreinforcingthefield's resilienceagainsttheconstantlychanginglandscapeofdigital manipulations. In essence, the incorporation of RNNs into multimedia forensic tools not only addresses the data limitationsassociatedwithCNNs but alsoenhances thetools' adaptabilityandprecisioninidentifyingdeeplearning-based manipulations. This evolution provides forensic experts with amorerobustmeanstodiscerntheauthenticityofmultimedia content,positioningthefieldattheforefrontofcombatingthe challenges posed by sophisticated digital manipulations in today's dynamic technological landscape

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Suganya P, Gokul P, Prasath R, & Sathya Ranjan Sahoo S (March-2024). Leveraging the Deep Fake Voice and Image for Robust Forgery Detection using Machine Learning. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 9(3), c217-c223. https://ijnrd.org/papers/IJNRD2403231.pdf

Volume 9 Issue 3, March-2024

Pages : c217-c223

Paper Reg. ID: IJNRD_214608

Published Paper Id: IJNRD2403231

Downloads: 000121991

Research Area: Engineering

Country: Puducherry, Puducherry, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2403231.pdf

Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2403231

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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