Enhancing Facial Recognition: Assessing CNNs for Detecting Image Manipulation

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This is an unedited manuscript accepted for publication and provided as an Article in Press for early access at the author’s request. The article will undergo copyediting, typesetting, and galley proof review before final publication. Please be aware that errors may be identified during production that could affect the content. All legal disclaimers of the journal apply.

Year : 2025 | Volume : 12 | Issue : 02 | Page : –
    By

    Karshit Bhaskar,

  • Anurag Aeron,

  • Hemang Shukla,

  • Kanika Yadav,

  • Iesha,

  1. Student, Department of Computer Science and Engineering, Meerut Institute of Engineering and Technology Meerut, Uttar Pradesh, India
  2. Professor, Department of Computer Science and Engineering, Meerut Institute of Engineering and Technology Meerut, Uttar Pradesh, India
  3. Student, Department of Computer Science and Engineering, Meerut Institute of Engineering and Technology Meerut, Uttar Pradesh, India
  4. Student, Department of Computer Science and Engineering, Meerut Institute of Engineering and Technology Meerut, Uttar Pradesh, India
  5. Student, Department of Computer Science and Engineering, Meerut Institute of Engineering and Technology Meerut, Uttar Pradesh, India

Abstract

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Deepfake technology, powered by highly advanced deep learning models, has raised significant concerns regarding media manipulation, identity theft, and the spread of online disinformation. Due to the increasing sophistication of deepfake content, traditional forensic methods often fail to detect such artificially generated images with high accuracy. Consequently, deep learning-based approaches have become essential in combating this challenge. This study compares six prominent deep learning architectures—VGG16, ResNet50, MobileNetV2, InceptionV3, EfficientNetB0, and DenseNet121—to analyze the effectiveness of Convolutional Neural Networks (CNNs) in detecting deepfake images. The models were trained on a dataset containing both real and manipulated facial images, incorporating various levels of complexity. Among the tested architectures, ResNet50 emerged as the top-performing model, achieving an impressive accuracy rate of 96.58%. The findings of this research highlight the critical role of deep learning in media verification and cybersecurity. By providing an in-depth analysis of CNN-based deepfake detection methods, this article lays the foundation for future advancements in safeguarding digital content against deceptive media attacks.

Keywords: Efficient Net, CNN, VGG19, Keras, Accuracy

[This article belongs to Journal of Image Processing & Pattern Recognition Progress ]

How to cite this article:
Karshit Bhaskar, Anurag Aeron, Hemang Shukla, Kanika Yadav, Iesha. Enhancing Facial Recognition: Assessing CNNs for Detecting Image Manipulation. Journal of Image Processing & Pattern Recognition Progress. 2025; 12(02):-.
How to cite this URL:
Karshit Bhaskar, Anurag Aeron, Hemang Shukla, Kanika Yadav, Iesha. Enhancing Facial Recognition: Assessing CNNs for Detecting Image Manipulation. Journal of Image Processing & Pattern Recognition Progress. 2025; 12(02):-. Available from: https://journals.stmjournals.com/joipprp/article=2025/view=0



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Regular Issue Subscription Review Article
Volume 12
Issue 02
Received 13/03/2025
Accepted 12/04/2025
Published 28/04/2025
Publication Time 46 Days
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