Efficient and Accurate Image Provenance Analysis: A Scalable Pipeline for Large-scale Images

TL;DR

This paper introduces a scalable, high-precision image provenance analysis pipeline that effectively traces image modifications and relationships in large-scale datasets with linear complexity and improved accuracy.

Contribution

The paper presents a novel end-to-end pipeline combining local features and artifact capturing, achieving linear scalability and higher accuracy in image provenance analysis.

Findings

Achieves 16.7-56.1% accuracy improvement over previous methods.

Demonstrates 3.0-second response time on 10 million images.

Outperforms state-of-the-art approaches in large-scale scenarios.

Abstract

The rapid proliferation of modified images on social networks that are driven by widely accessible editing tools demands robust forensic tools for digital governance. Image provenance analysis, which filters various query image variants and constructs a directed graph to trace their phylogeny history, has emerged as a critical solution. However, existing methods face two fundamental limitations: First, accuracy issues arise from overlooking heavily modified images due to low similarity while failing to exclude unrelated images and determine modification directions under diverse modification scenarios. Second, scalability bottlenecks stem from pairwise image analysis incurs quadratic complexity, hindering application in large-scale scenarios. This paper presents a scalable end-to-end pipeline for image provenance analysis that achieves high precision with linear complexity. This improves filtering effectiveness through modification relationship tracing, which enables the comprehensive discovery of image variants regardless of their visual similarity to the query. In addition, the proposed pipeline integrates local features matching and compression artifact capturing, enhancing robustness against diverse modifications and enabling accurate analysis of images' relationships. This allows the generation of a directed provenance graph that accurately characterizes the image's phylogeny history. Furthermore, by optimizing similarity calculations and eliminating redundant pairwise analysis during graph construction, the pipeline achieves a linear time complexity, ensuring its scalability for large-scale scenarios. Experiments demonstrate pipeline's superior performance, achieving a 16.7-56.1% accuracy improvement. Notably, it exhibits significant scalability with an average 3.0-second response time on 10 million scale images, which is far shorter than the SOTA approach's 12-minute duration.