  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 network (DEFL) to take
both the MHFs and randomly-initialized filters into consideration. The output
of the DEFL is fused with the semantic features to produce a compact
fingerprint. To make the compact fingerprint discriminative among different
models, we propose a Dual-Margin Contrastive (DMC) loss to tune our DEFL.
Finally, we propose a reference based fingerprint classification scheme for
image attribution. Experimental results demonstrate that it is indeed helpful
to use our MHFs for attributing the AI-generated images. The performance of our
proposed method is significantly better than the state-of-the-art for both the
closed-set and open-set image attribution, where only a small amount of images
are required for training.
