INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT International Peer Reviewed & Refereed Journals, Open Access Journal ISSN Approved Journal No: 2456-4184 | Impact factor: 8.76 | ESTD Year: 2016
Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.76 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI)
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
"Leveraging the Deep Fake Voice and Image for Robust Forgery Detection using Machine Learning", International Journal of Novel Research and Development (www.ijnrd.org), ISSN:2456-4184, Vol.9, Issue 3, page no.c217-c223, March-2024, Available :http://www.ijnrd.org/papers/IJNRD2403231.pdf
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2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.76 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
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