International Journal For Multidisciplinary Research

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Deepfake DEEPFAKE AND AI-GENERATED IMAGE DETECTION SYSTEM

Author(s) Ms. Iskand Wadhwa, Mr. Yash Choudhary, Ms. Prerna Chauhan, Ms. Anjali Singh, Mr. Sambit Sathua, Prof. Shaffy Bains
Country India
Abstract The rapid advancement of deep learning and gen-
erative artificial intelligence (AI) has led to the proliferation of
deepfake and AI-generated images, posing significant challenges
to digital media integrity, security, and trust. These technologies,
while beneficial in creative and entertainment domains, have
also been exploited for malicious purposes, including misinfor-
mation, identity theft, and fraud. To address these concerns,
this research proposes a robust and scalable Deepfake and
AI-Generated Image Detection System. Leveraging state-of-the-
art machine learning techniques, including convolutional neural
networks (CNNs), generative adversarial network (GAN) dis-
criminators, and transformer-based architectures, the system is
designed to identify subtle artifacts and inconsistencies inherent
in synthetic media. The proposed framework incorporates multi-
modal analysis, combining visual, spatial, and frequency-domain
features to enhance detection accuracy. Additionally, the system
is trained on a diverse and comprehensive dataset comprising
both publicly available and custom-generated deepfake and
AI-generated images to ensure generalizability across various
manipulation techniques. Experimental results demonstrate the
system’s effectiveness in achieving high precision and recall rates,
outperforming existing detection methods. This research con-
tributes to the ongoing efforts to combat digital misinformation
and uphold the authenticity of visual media in the age of AI.
Keywords component, formatting, style, styling, insert
Field Engineering
Published In Volume 7, Issue 2, March-April 2025
Published On 2025-04-20
DOI https://doi.org/10.36948/ijfmr.2025.v07i02.42245
Short DOI https://doi.org/g9f7p2
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