Abstract
Recently, generative adversarial networks (GANs) and its variants have shown impressive ability in image synthesis. The synthesized fake images spread widely on the Internet, and it is challenging for Internet users to identify the authenticity, which poses huge security risk to the society. However, compared with the powerful image synthesis technology, the detection of GAN-synthesized images is still in its infancy and face a variety of challenges. In this study, a method named fake images discriminator (FID) is proposed, which detects that GAN-synthesized fake images use the strong spectral correlation in the imaging process of natural color images. The proposed method first converts the color image into three color components of R, G, and B. Discrete wavelet transform (DWT) is then applied to RGB components separately. Finally, the correlation coefficient between the subband images is used as a feature vector for authenticity classification. Experimental results show that the proposed FID method achieves impressive effectiveness on the StyleGAN2-synthesized faces and multitype fake images synthesized with the state-of-the-art GANs. Also, the FID method exhibits good robustness against the four common perturbation attacks.
Highlights
With the remarkable development of artificial intelligence (AI) and progress of high-performance computing hardware, image synthesis technology has evolved dramatically. e Internet users share a large number of multimedia contents on social media every day
The precision, recall, F1-score, accuracy, AP, AUC, FPR, and FNR are reported. e AUC is used as a metric to evaluate the performance of the fake images discriminator (FID) method in tackling the four perturbation attacks and detecting other generative adversarial networks (GANs)-synthesized images
Research Directions e rapid development of AI technology makes it possible to produce fake content that can deceive humans, posing potential challenges to the society and people. is study proposes a method for detecting GAN-synthesized fake images based on Discrete wavelet transform (DWT) and the standard correlation coefficient
Summary
With the remarkable development of artificial intelligence (AI) and progress of high-performance computing hardware, image synthesis technology has evolved dramatically. e Internet users share a large number of multimedia contents on social media every day. If these fake contents are disseminated as news materials, they will damage the reputation of news organizations and the public’s confidence in the media and even mislead the public opinion and disturb the social order. E increasingly open network environment creates an ideal space for the spread of fake information. In the countries such as Britain and France, there have been cases of using deeplearning forgery technology to produce fake images, deceive the public to even conduct espionage. It is extremely urgent to find effective techniques for detection of fake images
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