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A Multilayer Microfluidic Platform for the Conduction of Prolonged Cell-Free Gene Expression

A Multilayer Microfluidic Platform for the Conduction of Prolonged Cell-Free Gene Expression

Published on: October 6, 2019

Improving synthetic media generation and detection using generative adversarial networks.

Rabbia Zia¹, Mariam Rehman¹, Afzaal Hussain¹

¹Department of Information Technology, Government College University Faisalabad, Punjab, Pakistan.

Peerj. Computer Science

|September 24, 2024

Summary

This study introduces an improved generative adversarial network (GAN) for detecting deepfakes, enhancing accuracy in differentiating real from synthetic images. The optimized model significantly reduces risks associated with AI-generated content.

Keywords:

Deep neural networks DeepFake Generative adversarial networks Image manipulation Manipulation detection

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Area of Science:

Computer Vision
Artificial Intelligence
Machine Learning

Background:

Synthetic images, or deepfakes, created using generative models and deep learning pose risks through misinformation and violations of social media regulations.
Current methods for deepfake detection require improvement in accuracy and robustness.

Purpose of the Study:

To propose an improved generative adversarial network (GAN) model for enhanced accuracy in distinguishing real from synthetic images.
To address the challenges posed by deepfakes by focusing on data augmentation and label smoothing strategies during GAN training.

Main Methods:

Utilized a deep convolutional generative adversarial network (DCGAN) as the base model.
Implemented data augmentation and label smoothing techniques for GAN training.
Optimized model parameters for enhanced performance.

Main Results:

The proposed GAN model demonstrated superior performance compared to traditional GANs.
Achieved a Fréchet Inception Distance (FID) score of 55.67 and an accuracy of 98.82% on benchmark datasets.
Attained an F1-score of 0.99 in synthetic image detection.

Conclusions:

The developed GAN framework is effective for both synthetic image generation and detection.
The optimized model significantly reduces risks associated with deepfakes.
This research contributes an effective solution for combating the spread of false information through synthetic media.