A Framework for False Image Detection with Sample-Oriented Intelligent Adversarial

Abstract

To develop algorithms capable of automatically detecting and evaluating the authenticity of images and videos, researchers have focused on false image detection algorithms. These algorithms aim to identify the authenticity of images by distinguishing between real images and forged ones generated using false generation algorithms. In this paper, the main focus is on implementing a single-frame authenticated image detector using the concept of migration learning. The detector utilizes Inception ResNet v2, a target classification network pre-trained on a self-built military scene dataset. To enhance the dataset, a series of graphical enhancement algorithms are employed, enabling the classification network to learn the crucial differences between real and forged images. Additionally, Focal Loss is introduced to balance the dataset for various GAN-based image generation algorithms. As a result, the final forged target image detector achieves an impressive classification accuracy of 0.8908 on a large-scale sample test set.