Attribution as Retrieval: Model-Agnostic AI-Generated Image Attribution

TL;DR

This work presents a new paradigm for AI-generated image attribution by formulating it as an instance retrieval problem instead of a conventional image classification problem, and proposes an efficient model-agnostic framework, called Low-bIt-plane-based Deepfake Attribution (LIDA).

Abstract

With the rapid advancement of AIGC technologies, image forensics will encounter unprecedented challenges. Traditional methods are incapable of dealing with increasingly realistic images generated by rapidly evolving image generation techniques. To facilitate the identification of AI-generated images and the attribution of their source models, generative image watermarking and AI-generated image attribution have emerged as key research focuses in recent years. However, existing methods are model-dependent, requiring access to the generative models and lacking generality and scalability to new and unseen generators. To address these limitations, this work presents a new paradigm for AI-generated image attribution by formulating it as an instance retrieval problem instead of a conventional image classification problem. We propose an efficient model-agnostic framework, called Low-bIt-plane-based Deepfake Attribution (LIDA). The input to LIDA is produced by Low-Bit Fingerprint Generation module, while the training involves Unsupervised Pre-Training followed by subsequent Few-Shot Attribution Adaptation. Comprehensive experiments demonstrate that LIDA achieves state-of-the-art performance for both Deepfake detection and image attribution under zero- and few-shot settings. The code is at https://github.com/hongsong-wang/LIDA

Figures (19)

• Figure 1: Comparison between generative image watermarking and our retrieval-based AI-generated image attribution. Our framework is concise, versatile, and easily adapted to new image generators.
• Figure 2: Pipeline of the proposed model-agnostic framework LIDA for AI-generated image attribution. LIDA treats image attribution as an instance retrieval problem, and uses low-bit-plane-based generative fingerprint as the input. The training stage consists of two consecutive steps: (a) unsupervised pre-training and (b) few-shot attribution adaptation.
• Figure 3: Comparison of AI-generated images and low-bit generative fingerprints from different image generators. Generators include Stable Diffusion rombach2022high, ADM dhariwal2021diffusion, and Wukong wukong2022.
• Figure 4: Feature distribution of images from different sources for (a) RGB images and (b) low-bit generative fingerprints.
• Figure 5: