Keywords:
image forgery
digital manipulation
detection methods

  • Abstract

    Digital social platforms such as Facebook, Instagram, Snapchat, etc., driven by multimedia content, has led to billions of photos being shared annually. The ease of image modification has resulted in ‘deepfakes’, causing potential misinformation, emotional distress, and credibility loss. Image forgery has become a widespread problem with the advent of digital manipulation tools. Manipulated pictures are a problem in the digital world, demanding strong detection methods. This survey explores this important field and offers an extensive overview of state-of-the-art methods for identifying picture forgeries. Breaking down well-known techniques such as classic feature analysis, which unmask significant differences by examining pixels in depth and statistical irregularities. Moreover, this paper explores the revolutionary potential of deep learning by highlighting how convolutional neural networks (CNNs) can analyze pictures with unparalleled accuracy and reveal previously unseen alterations. By providing readers with a comprehensive grasp of the most recent developments in picture forgery detection with the datasets, this article gives them the knowledge they need to successfully negotiate the always changing field of visual deception. This survey acts as a launchpad for further research and development.

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Timothy, D. P., & Santra, A. K. (2024). Unveiling image forgery with cutting-edge techniques-a survey. Multidisciplinary Reviews, 7(10), 2024220. https://doi.org/10.31893/multirev.2024220
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