Protecting the trust and credibility of data by tracking forgery trace based on GANs

Abstract

With the advent of the 5G Internet of Things era, communication and social interaction in our daily life have changed a lot, and a large amount of social data is transmitted to the Internet. At the same time, with the rapid development of deep forgery technology, a new generation of social data trust crisis has also followed. Therefore, how to ensure the trust and credibility of social data in the 5G Internet of Things era is an urgent problem to be solved. This paper proposes a new method for forgery detection based on GANs. We first discover the hidden gradient information in the grayscale image of the forged image and use this gradient information to guide the generation of forged traces. In the classifier, we replace the traditional binary loss with the focal loss that can focus on difficult-to-classify samples, which can achieve accurate classification when the real and fake samples are unbalanced. Experimental results show that the proposed method can achieve high accuracy on the DeeperForensics dataset and with the highest accuracy is 98%.

Keywords

• Forgery detection
• Trace generation
• Social data
• Privacy protection