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Stream/Area:
-
Subject:
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Paper ID:
IJSREM62984
Volume:
10 |
5 |
May 2026
Pages:
1-10
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
Department of Computer Science and Engineering
Shri Shankaracharya Technical Campus
Department of Computer Science and Engineering
Shri Shankaracharya Technical Campus
Department of Computer Science and Engineering
Shri Shankaracharya Technical Campus
The rapid advancement of artificial intelligence and deep learning technologies has led to the emergence of synthetic audiovisual media, commonly referred too as "deepfakes." This technology enables the creation of highly realistic fake videos, images, and audio that are increasingly indistinguishable from authentic content. While deepfake technology has promising applications in entertainment, education, and cultural preservation, i also poses significant threats to privacy, security, and democratic processes. This paper provides a comprehensive overview of deepfake and synthetic media technologies, examining their underlying mechanisms, diverse applications, associated risks, and the evolving regulatory landscape. Furthermore, it explores mitigation strategies and proposes recommendations for addressing the challenges posed by malicious use of synthetic media. The analysis reveals that effective countermeasures require a multi-faceted approach encompassing technological innovation, legal frameworks, user education, and international cooperation.
KEYWORDS
This paper is presented S.M.A.R.T. (Synthetic Media Rtifact Recognition Tool), a technically advanced framework for the detection and analysis of synthetic media. The system integrates a multi stage pipeline, including MTCNN based facial alignmen, transformer influenced spatial feature extraction, and bidirectional recurrent modelling, to convert raw video data into a verifiable forensic report. By supporting multimodal inputs and utilising Grad-CAM for visual explainability, the platform significantly enhance the ability of researchers and forensic analyst to identify malicious forgeries. The high accuracy achieved on benchmark datasets validates the effectiveness of identifying “artifacts” as a superior method to simple binary classification. Funthermore, the integration of AI powered analytics provides deeper insights into the specific generative signatures used, aiding in the borader effort to maintain digital integrity. Future work will focus on extending the system to support real time edge detection for mobile devices, automated detection of synthetic audio only conten, and the integration of blockchain based watermarking to establish a permanent chain of custody for authentic media.
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Publication Date
5/9/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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Conflict of Interest
The authors declare no conflict of interest related to this research work.
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No funding information available for this research.
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