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<p>In today’s digital era, communication relies heavily on images shared across various platforms. However, digital images are susceptible to alteration and forgery due to the widespread availability of sophisticated image editing software and tools. Such substantial tampering can significantly impact the accuracy of information provided. Therefore, robust procedures are imperative for analyzing images and testing their authenticity, especially in legal areas where evidence tampering has severe consequences. Image forgery detection and localization have become all the more crucial due to rapidly growing usage of smartphones and camera devices. Our survey comprehensively examines a diverse range of image manipulation attacks, including copy-move, splicing, compression, geometric transformation, cropping, recapturing, inpainting, and deepfake techniques, as well as the significant forensic problem of source camera identification. To address these challenges, we explore numerous approaches spanning from traditional techniques to cutting-edge deep learning-based solutions. Our analysis provides insights into effectiveness of individual solution approaches for different attacks offering a unified overview under a comprehensive framework.</p>

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Multi-approach survey and in-depth analysis of image forgery detection techniques

  • 期刊论文

  • 发表日期：

    2025年6月9日

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  • Arundhati Bhowal,
  • Ruchira Naskar,
  • Sarmistha Neogy

摘要

In today’s digital era, communication relies heavily on images shared across various platforms. However, digital images are susceptible to alteration and forgery due to the widespread availability of sophisticated image editing software and tools. Such substantial tampering can significantly impact the accuracy of information provided. Therefore, robust procedures are imperative for analyzing images and testing their authenticity, especially in legal areas where evidence tampering has severe consequences. Image forgery detection and localization have become all the more crucial due to rapidly growing usage of smartphones and camera devices. Our survey comprehensively examines a diverse range of image manipulation attacks, including copy-move, splicing, compression, geometric transformation, cropping, recapturing, inpainting, and deepfake techniques, as well as the significant forensic problem of source camera identification. To address these challenges, we explore numerous approaches spanning from traditional techniques to cutting-edge deep learning-based solutions. Our analysis provides insights into effectiveness of individual solution approaches for different attacks offering a unified overview under a comprehensive framework.