Detecting Generated Images by Real Images Only
Xiuli Bi, Bo Liu, Fan Yang, Bin Xiao, Weisheng Li, Gao, Huang, Pamela C. Cosman

TL;DR
This paper introduces a novel approach for detecting generated images by modeling only real images, enabling efficient, robust detection across various generative models with minimal training data.
Contribution
The method uniquely focuses on real images to identify a common feature subspace, improving detection efficiency and generalization without relying on generated images during training.
Findings
Achieves high detection accuracy with only real images and minimal training data.
Demonstrates robustness against post-processing techniques.
Performs well on emerging generative models.
Abstract
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…
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Taxonomy
TopicsAdvanced Image and Video Retrieval Techniques · Generative Adversarial Networks and Image Synthesis · Image Retrieval and Classification Techniques
