Digitally retouching images has become a popular trend, with people posting
altered images on social media and even magazines posting flawless facial
images of celebrities. Further, with advancements in Generative Adversarial
Networks (GANs), now changing attributes and retouching have become very easy.
Such synthetic alterations have adverse effect on face recognition algorithms.
While researchers have proposed to detect image tampering, detecting GANs
generated images has still not been explored. This paper proposes a supervised
deep learning algorithm using Convolutional Neural Networks (CNNs) to detect
synthetically altered images. The algorithm yields an accuracy of 99.65% on
detecting retouching on the ND-IIITD dataset. It outperforms the previous state
of the art which reported an accuracy of 87% on the database. For
distinguishing between real images and images generated using GANs, the
proposed algorithm yields an accuracy of 99.83%.

本文提出了使用卷积神经网络的监督深度学习算法来检测人工合成图像，并在 ND-IIITD 数据集上获得了 99.65％的准确度，对于区分真实图像和使用生成对抗网络（GANs）生成的图像的准确度达到了 99.83％。