Enhancing Digital Integrity: A Robust Deepfake Image Detection System

  • Unique Paper ID: 176270
  • Volume: 11
  • Issue: 11
  • PageNo: 5109-5115
  • Abstract:
  • Deepfake technology has advanced significantly, making the detection of manipulated media an important challenge in the digital world. The increasing use of fakes poses a severe threat to information integrity, privacy, & security. This study investigates the potential of algorithmic detectability in combating deepfakes, developing detection strategies, & evolving obstacles in this domain. We present a detailed analysis of machine learning & deep learning approaches to deepfake detection, emphasizing the usefulness & limits of the existing methodologies. In addition, we examine the significance of human perception in detecting deepfakes as well as the use of explainable AI (XAI) to increase transparency and trust. The findings suggest that while deep learning-based methods have shown great progress, adversarial techniques, a machine learning method that aims to trick machine learning models by providing deceptive input, remain a serious challenge. The detection model is experimentally evaluated using industry-standard performance metrics such as accuracy, precision, recall, F1 score, and AUC-ROC. To show advancements in accuracy and resilience, a comparison with the latest and most advanced deepfake detection models is conducted. Future research should focus on strong, real-time detection systems that can adapt to changing deepfake technologies. By analyzing cutting-edge research & experimental data, this study hopes to help design more resilient & effective deepfake detection systems.

Cite This Article

  • ISSN: 2349-6002
  • Volume: 11
  • Issue: 11
  • PageNo: 5109-5115

Enhancing Digital Integrity: A Robust Deepfake Image Detection System

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