As deep learning technology continues to evolve, the images yielded by
generative models are becoming more and more realistic, triggering people to
question the authenticity of images. Existing generated image detection methods
detect visual artifacts in generated images or learn discriminative features
from both real and generated images by massive training. This learning paradigm
will result in efficiency and generalization issues, making detection methods
always lag behind generation methods. This paper approaches the generated image
detection problem from a new perspective: Start from real images. By finding
the commonality of real images and mapping them to a dense subspace in feature
space, the goal is that generated images, regardless of their generative model,
are then projected outside the subspace. As a result, images from different
generative models can be detected, solving some long-existing problems in the
field. Experimental results show that although our method was trained only by
real images and uses 99.9\% less training data than other deep learning-based
methods, it can compete with state-of-the-art methods and shows excellent
performance in detecting emerging generative models with high inference
efficiency. Moreover, the proposed method shows robustness against various
post-processing. These advantages allow the method to be used in real-world
scenarios.

基于真实图像的生成图像检测方法，在特征空间中找到真实图像的共性，并将其映射到稠密子空间，从而使得无论生成模型如何，生成图像都能够被投影到子空间之外，实验证明所提方法能够在高推理效率的同时，通过使用较少的训练数据与最新生成模型竞争，并具有抗各种后处理的鲁棒性。