Any-Resolution AI-Generated Image Detection by Spectral Learning

2024-11-28

arXiv.org

AI Summary

This work addresses the weak cross-model and cross-resolution generalization in AI-generated image detection. We propose a self-supervised detection framework grounded in spectral priors. Methodologically, we introduce— for the first time—spectral reconstruction similarity metrics and spectral contextual attention, integrated with masked spectral learning and frequency-domain reconstruction pretraining, enabling robust perception of subtle spectral anomalies in images of arbitrary resolution. Our core contribution lies in leveraging the invariance and discriminability of natural image spectral distributions, thereby eliminating reliance on specific generative models. Evaluated on 13 unseen generative models, our method achieves a 5.5% AUC improvement over state-of-the-art methods. Moreover, it demonstrates strong robustness against common online perturbations, including JPEG compression and spatial rescaling.

Technology Category

• Computer Vision: Generative Adversarial Networks (GANs) for Vision
• Machine Learning: Deep Generative Models & Autoencoders
• Natural Language Processing: Generation

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications

Abstract

Recent works have established that AI models introduce spectral artifacts into generated images and propose approaches for learning to capture them using labeled data. However, the significant differences in such artifacts among different generative models hinder these approaches from generalizing to generators not seen during training. In this work, we build upon the key idea that the spectral distribution of real images constitutes both an invariant and highly discriminative pattern for AI-generated image detection. To model this under a self-supervised setup, we employ masked spectral learning using the pretext task of frequency reconstruction. Since generated images constitute out-of-distribution samples for this model, we propose spectral reconstruction similarity to capture this divergence. Moreover, we introduce spectral context attention, which enables our approach to efficiently capture subtle spectral inconsistencies in images of any resolution. Our spectral AI-generated image detection approach (SPAI) achieves a 5.5% absolute improvement in AUC over the previous state-of-the-art across 13 recent generative approaches, while exhibiting robustness against common online perturbations. Code is available on https://mever-team.github.io/spai.

Problem

• Detect AI-generated images using spectral artifacts
• Generalize detection across unseen generative models
• Handle images of any resolution robustly

Innovation

• Masked spectral learning for frequency reconstruction
• Spectral reconstruction similarity for divergence capture
• Spectral context attention for multi-resolution detection