TY - GEN
T1 - Unmasking Digital Deceptions
T2 - 2024 IEEE International Conference on Electronic Systems and Intelligent Computing, ICESIC 2024
AU - Chaudhary, Sneha
AU - Chaurasiya, Komal
AU - Sriram, Suthir
AU - Ravikumar, S.
AU - Nivethitha, V.
AU - Thangavel, M.
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - The general accessibility of social networking sites like Facebook, Myspace and TikTok has transformed the way information is communicated, but it has also made it simpler to spread misleading information, thanks to cutting-edge technologies like deepfakes. The use of artificial intelligence in deepfake technology produces material that is remarkably life- like but fake, making it difficult to identify and remove fraudulent content. The evolution of deepfake technology is reviewed in this study, starting with early breakthroughs like the Video Rewrite Program and continuing with more current developments like the Synthesizing Obama and Face2Face projects. We provide a comprehensive analysis of the state-of-the-art detection strategies, namely Region-based CNNs (RCNNs), Conventional Neural Networks (CNNs), and hybrid approaches that combine various deep learning techniques. Though these methods have advanced, there are still issues with accuracy, integration complexity, and adaptability to new deepfake techniques. This paper suggests a novel approach that integrates image, video, and audio analysis into a single detection framework in order to overcome these difficulties. The new approach is directed towards improvement of detection accuracy and the lowering of the false alarm using advanced and deep learning engines along with the real time processing requirements. Early results indicate that the detection capabilities have significantly improved compared to existing solutions operating in real time environment while having less latency and higher recall and precision rates. This method is more effective for deepfake detection and at the same time helps in augmenting the comprehensiveness and efficiency of the system there by making it suitable for wider uses. The study emphasizes the presence of the need for the evolution of static approaches towards the effective defection of sophisticated false information and the justification of new possibilities in the fight against disinformation and protection of information space in general.
AB - The general accessibility of social networking sites like Facebook, Myspace and TikTok has transformed the way information is communicated, but it has also made it simpler to spread misleading information, thanks to cutting-edge technologies like deepfakes. The use of artificial intelligence in deepfake technology produces material that is remarkably life- like but fake, making it difficult to identify and remove fraudulent content. The evolution of deepfake technology is reviewed in this study, starting with early breakthroughs like the Video Rewrite Program and continuing with more current developments like the Synthesizing Obama and Face2Face projects. We provide a comprehensive analysis of the state-of-the-art detection strategies, namely Region-based CNNs (RCNNs), Conventional Neural Networks (CNNs), and hybrid approaches that combine various deep learning techniques. Though these methods have advanced, there are still issues with accuracy, integration complexity, and adaptability to new deepfake techniques. This paper suggests a novel approach that integrates image, video, and audio analysis into a single detection framework in order to overcome these difficulties. The new approach is directed towards improvement of detection accuracy and the lowering of the false alarm using advanced and deep learning engines along with the real time processing requirements. Early results indicate that the detection capabilities have significantly improved compared to existing solutions operating in real time environment while having less latency and higher recall and precision rates. This method is more effective for deepfake detection and at the same time helps in augmenting the comprehensiveness and efficiency of the system there by making it suitable for wider uses. The study emphasizes the presence of the need for the evolution of static approaches towards the effective defection of sophisticated false information and the justification of new possibilities in the fight against disinformation and protection of information space in general.
KW - Analysis
KW - Artificial Intelligence
KW - Deepfake
KW - Hoaxes
KW - Neural Networks
UR - https://www.scopus.com/pages/publications/85217861439
UR - https://www.scopus.com/pages/publications/85217861439#tab=citedBy
U2 - 10.1109/ICESIC61777.2024.10846333
DO - 10.1109/ICESIC61777.2024.10846333
M3 - Conference contribution
AN - SCOPUS:85217861439
T3 - IEEE International Conference on Electronic Systems and Intelligent Computing, ICESIC 2024 - Proceedings
SP - 202
EP - 207
BT - IEEE International Conference on Electronic Systems and Intelligent Computing, ICESIC 2024 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 22 November 2024 through 23 November 2024
ER -