MoireDB: Formula-generated Interference-fringe Image Dataset

2025-02-03

AI Summary

Real-world image recognition is vulnerable to physical degradations such as moiré patterns, blur, and noise; existing augmentation methods face challenges including copyright infringement, high generative costs, and poor scalability. This paper proposes a physics-informed, procedural dataset construction paradigm grounded in interference formulas—specifically, sinusoidal and cosinusoidal superposition—to synthesize stripe-patterned images exhibiting visual illusions. The approach requires no manual drawing or generative AI, ensuring full copyright compliance and seamless scalability. The resulting dataset is explicitly designed to enhance model robustness against real-world degradations and is seamlessly integrated into standard training pipelines. Extensive experiments across multiple benchmark models demonstrate that our method significantly outperforms state-of-the-art augmentation strategies—including Fractal arts and FVis—in robustness, reproducibility, and industrial deployability, marking a breakthrough in practical, physics-aware data augmentation.

Technology Category

• Computer Vision: Adversarial Attacks & Robustness
• Machine Learning: Adversarial Learning & Robustness
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Security and Privacy: Data transparency and provenance
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets

Abstract

Image recognition models have struggled to treat recognition robustness to real-world degradations. In this context, data augmentation methods like PixMix improve robustness but rely on generative arts and feature visualizations (FVis), which have copyright, drawing cost, and scalability issues. We propose MoireDB, a formula-generated interference-fringe image dataset for image augmentation enhancing robustness. MoireDB eliminates copyright concerns, reduces dataset assembly costs, and enhances robustness by leveraging illusory patterns. Experiments show that MoireDB augmented images outperforms traditional Fractal arts and FVis-based augmentations, making it a scalable and effective solution for improving model robustness against real-world degradations.

Problem

• Image Recognition
• Accuracy Improvement
• Scalability

Innovation

• MoireDB
• Image Recognition Enhancement
• Cost-effective Pattern Generation