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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM64237
Volume:
10 |
5 |
May 2026
Pages:
1-4
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.
Computer Science and Engineering
Ashokrao Mane Group of Institutions, Vathar
Computer Science and Engineering
Ashokrao Mane Group of Institutions, Vathar
Computer Science and Engineering
Ashokrao Mane Group of Institutions, Vathar
Computer Science and Engineering
Ashokrao Mane Group of Institutions, Vathar
Computer Science and Engineering
Ashokrao Mane Group of Institutions, Vathar
Computer Science and Engineering
Ashokrao Mane Group of Institutions, Vathar
The rapid advancement of Artificial Intelligence (AI) has enabled the creation of highly realistic synthetic media, including images, videos, and audio, through technologies such as Generative Adversarial Networks (GANs), diffusion models, and voice cloning systems. While these innovations offer significant benefits in entertainment, education, and communication, they also introduce serious challenges such as deepfakes, misinformation, identity theft, cyber fraud, and digital manipulation. The increasing accessibility of AI-generated content has made it difficult for users to distinguish authentic media from manipulated content, creating a critical need for reliable verification systems. This paper presents Reality Shield, an intelligent multimodal AI-generated content detection platform designed to identify manipulated images, videos, and audio files. The proposed system integrates advanced machine learning and deep learning algorithms, including Convolutional Neural Networks (CNN), Support Vector Machines (SVM), Random Forest, Logistic Regression, Bayesian classifiers, and Vision Transformer (ViT), to analyze multimedia content and classify it as real or fake. The system performs media preprocessing, feature extraction, classification, and confidence-score generation to provide accurate and explainable detection results. Reality Shield is developed using a React-based frontend and REST API-driven backend architecture, ensuring a scalable, user-friendly, and real-time detection environment. Experimental evaluation demonstrates that the proposed framework achieves high detection accuracy and effectively identifies visual, temporal, and acoustic inconsistencies commonly present in AI-generated media. By supporting image, video, and audio verification within a single platform, Reality Shield contributes to enhancing digital trust, combating misinformation, strengthening cybersecurity, and promoting responsible use of artificial intelligence in modern digital ecosystems.
KEYWORDS
The proposed system, Reality Shield: AI Generated Content Detector, provides an effective solution for detecting AI-generated images, videos, and audio using machine learning and deep learning techniques. The system integrates algorithms such as CNN, SVM, Random Forest, Logistic Regression, Bayesian classifiers, and Vision Transformer to improve detection accuracy and reliability. The project successfully performs multimedia preprocessing, feature extraction, classification, and real-time prediction using a React-based frontend and REST API backend. Experimental results show that the system can accurately identify manipulated multimedia content and provide confidence-based predictions. Reality Shield contributes toward reducing misinformation, improving cybersecurity, and strengthening digital trust in modern digital environments.
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Publication Date
5/28/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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The authors declare no conflict of interest related to this research work.
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