• Dr R.G.Suresh Kumar
• D.Lokhitha

Multimedia forensics has made remarkable strides 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 convolutional neural networks (CNNs), which are commonly 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 particularly evident 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 discerning subtle alterations in multimedia content over time, 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. The integration of RNNs into multimedia forensic tools represents a promising avenue for reinforcing the field's resilience against the constantly changing landscape of digital manipulations. In essence, the incorporation of RNNs into multimedia forensic tools not only addresses the data limitations associated with CNNs but also enhances the tools' adaptability and precision in identifying deep learning-based manipulations. This evolution provides forensic experts with a more robust means to discern the authenticity of multimedia content, positioning the field at the forefront of combating the challenges posed by sophisticated digital manipulations in today's dynamic technological landscape.

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

"AUDIO AUTHENTICITY VERIFICATION THROUGH DEVICE TRACES AND RNN-BASED ANTI-FORGERY", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 5, page no.699-707, May 2019, Available :http://www.jetir.org/papers/JETIR1905Y96.pdf

"AUDIO AUTHENTICITY VERIFICATION THROUGH DEVICE TRACES AND RNN-BASED ANTI-FORGERY", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 5, page no. pp699-707, May 2019, Available at : http://www.jetir.org/papers/JETIR1905Y96.pdf