Are handcrafted filters helpful for attributing AI-generated images?
Jialiang Li, Haoyue Wang, Sheng Li, Zhenxing Qian, Xinpeng Zhang and, Athanasios V. Vasilakos

TL;DR
This paper investigates the effectiveness of handcrafted filters, specifically Multi-Directional High-Pass Filters, in improving AI-generated image attribution, reducing training data needs, and enhancing model fingerprint discrimination.
Contribution
The authors introduce MHFs and a DEFL network with DMC loss, demonstrating improved attribution performance with less training data compared to existing methods.
Findings
MHFs effectively extract subtle AI image fingerprints.
The proposed method outperforms state-of-the-art in closed-set and open-set attribution.
Significant reduction in training data required for accurate attribution.
Abstract
Recently, a vast number of image generation models have been proposed, which raises concerns regarding the misuse of these artificial intelligence (AI) techniques for generating fake images. To attribute the AI-generated images, existing schemes usually design and train deep neural networks (DNNs) to learn the model fingerprints, which usually requires a large amount of data for effective learning. In this paper, we aim to answer the following two questions for AI-generated image attribution, 1) is it possible to design useful handcrafted filters to facilitate the fingerprint learning? and 2) how we could reduce the amount of training data after we incorporate the handcrafted filters? We first propose a set of Multi-Directional High-Pass Filters (MHFs) which are capable to extract the subtle fingerprints from various directions. Then, we propose a Directional Enhanced Feature Learning…
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Taxonomy
TopicsGenerative Adversarial Networks and Image Synthesis · AI in cancer detection
MethodsSparse Evolutionary Training
