计算机科学
人工智能
对抗制
生成语法
图像(数学)
生成对抗网络
深度学习
模式识别(心理学)
人工神经网络
傅里叶变换
深层神经网络
计算机视觉
数学
数学分析
作者
Chin‐Shyurng Fahn,Tzu‐Chin Wu
标识
DOI:10.1109/icmlc56445.2022.9941295
摘要
In this paper, the deep learning-based method for forgery image detection is presented. First, we respectively do discrete Fourier transform for both real images and the forgery images generated from the generative adversarial networks. Then the obtained Fourier spectrums are fed to deep neural networks for model training. In order to enhance the detection capability of the model, we incorporate contrastive learning to make the model directly learns the difference between real and forgery images. Four kinds of generative adversarial networks (GANs), namely DCGAN, CycleGAN, AutoGAN, and Mixed GAN, are chosen to generate forgery images for testing our proposed method. The experimental results reveal that the average accuracy rate reaches 99.5% using our proposed method to detect the four kinds of GAN-generated images. Compared with the state-of-the-art forgery image detection method, our proposed method can more widely detect forgery images derived from different sources.
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