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Updated: Jul 12, 2025

Artificial Intelligence Approaches to Assessing Primary Cilia
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AI vs. AI: Can AI Detect AI-Generated Images?

Samah S Baraheem1,2, Tam V Nguyen2

  • 1Department of Computer Science, Umm Al-Qura University, Prince Sultan Bin Abdulaziz Road, Mecca 21421, Makkah, Saudi Arabia.

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|October 27, 2023
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Summary
This summary is machine-generated.

This study introduces a Convolutional Neural Network (CNN) framework to detect AI-generated images, achieving 100% accuracy. This reliable detection method is crucial for verifying image authenticity in the age of Generative Adversarial Networks (GANs).

Keywords:
GAN image localizationGAN-generated images detectionconvolutional neural networksdetection of computer-generated imagesfake AI-generated images recognitionfake and real detection

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Machine Learning

Background:

  • Generative Adversarial Networks (GANs) excel at creating realistic synthetic images, posing challenges to authenticity and security.
  • The widespread internet distribution of AI-generated images necessitates robust detection methods.
  • Automated detection systems are vital for evaluating image synthesis models and ensuring content integrity.

Purpose of the Study:

  • To develop a reliable framework for distinguishing AI-generated images from real ones.
  • To create an effective evaluation tool for image synthesis models.
  • To enhance the security and authenticity verification of digital media.

Main Methods:

  • Utilized Convolutional Neural Networks (CNNs) for image classification.
  • Collected diverse GAN-generated images across various tasks and architectures for generalized detection.
  • Applied transfer learning and integrated Class Activation Maps (CAM) to identify discriminative regions for classification.
  • Fine-tuned a pre-trained EfficientNetB4 model using Adam optimizer, with specific learning rates, batch size, and epochs, incorporating data augmentation and learning rate reduction.

Main Results:

  • Achieved 100% accuracy on the Real or Synthetic Images (RSI) dataset.
  • Demonstrated superior performance and accuracy on various other datasets and configurations.
  • The developed CNN framework effectively identifies GAN-generated images.

Conclusions:

  • The proposed CNN framework reliably detects AI-generated images, offering a valuable evaluation tool for image synthesis.
  • The method shows high accuracy and generalization capabilities, crucial for combating the spread of synthetic media.
  • EfficientNetB4 fine-tuned with specific parameters proved highly effective for GAN image detection.