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In the modern world the advancement and development of digital manipulation tools has led to an increase in sophisticated image tampering, creating significant concerns over the authenticity of visual content, especially in areas like journalism, law, and media. For instance, consider the dissipate of phony and altered images on online social networks has raised alarms about misinformation, where fabricated images are used to deceive the public or manipulate public opinion. To tackle these growing challenges, we propose an Innovative deep learning framework incorporating Convolutional Neural Networks (CNNs) and Generative Adversarial Networks (GANs) for detecting tampered images with great accuracy. CNNs are used to identify inconsistencies in pixel patterns and structural anomalies, enabling effective classification of tampered images. Meanwhile, GANs are employed to reconstruct tampered regions, providing insights into potential restoration of the original content and deeper analysis of the alterations. Our approach enhances tampering detection accuracy and offers interpretable visualizations of manipulated areas, making it a powerful tool in combating the increasing prevalence of image manipulation in real-time applications, such as detecting fake news and warranting the authenticity of visual illustrations in the digitalised age.

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Identifying Image Tampering Using Deep Learning - Combinational Approach of CNN & GAN

  • 会议论文

  • 发表日期：

    2026年

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  • N. Gayathri,
  • B. Devayani,
  • T. Dhanushya,
  • R. Dharshini,
  • V. Gayathri

摘要

In the modern world the advancement and development of digital manipulation tools has led to an increase in sophisticated image tampering, creating significant concerns over the authenticity of visual content, especially in areas like journalism, law, and media. For instance, consider the dissipate of phony and altered images on online social networks has raised alarms about misinformation, where fabricated images are used to deceive the public or manipulate public opinion. To tackle these growing challenges, we propose an Innovative deep learning framework incorporating Convolutional Neural Networks (CNNs) and Generative Adversarial Networks (GANs) for detecting tampered images with great accuracy. CNNs are used to identify inconsistencies in pixel patterns and structural anomalies, enabling effective classification of tampered images. Meanwhile, GANs are employed to reconstruct tampered regions, providing insights into potential restoration of the original content and deeper analysis of the alterations. Our approach enhances tampering detection accuracy and offers interpretable visualizations of manipulated areas, making it a powerful tool in combating the increasing prevalence of image manipulation in real-time applications, such as detecting fake news and warranting the authenticity of visual illustrations in the digitalised age.