Enhancing Image Authenticity Verification through Deep Learning Techniques: A Study on the Detection and Mitigation of Fake Images
Kasheena Mulla, Pratichee Mishra, Shruti Kharche, Prof. Chaitanya Garware
Fake image detection, CNN, GAN, image forgery, media authenticity, journalistic integrity, content curation.
In the realm of digital media, the pervasive dissemination of deceptive imagery poses a significant challenge for journalists and content curators striving to maintain the accuracy of information. This paper introduces an innovative image authenticity verification system that harnesses deep learning techniques, specifically convolutional neural networks (CNNs) and generative adversarial networks (GANs). Through a comprehensive literature review, including seminal works in image forgery detection, the proposed solution prioritizes real-time analysis, an intuitive user interface, and ethical considerations to combat the proliferation of fake images. Future prospects are explored, encompassing efforts to mitigate biases, extend the system to verify various media formats, and underline the importance of upholding credibility and trustworthiness in journalistic and curated content. This research addresses the pressing need for robust verification mechanisms in the face of rampant misinformation, providing a promising avenue for enhancing the integrity of digital media platforms.
Article Details
Unique Paper ID: 163719

Publication Volume & Issue: Volume 10, Issue 11

Page(s): 1744 - 1750
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