Detecting Near-Duplicate Face Images

TL;DR

This paper presents a novel graph-theoretic method for detecting near-duplicate face images by constructing Image Phylogeny Trees and Forests, improving accuracy in identifying original images and their relationships despite transformations.

Contribution

Introduces a new approach using IPTs and IPFs for near-duplicate face image detection, significantly enhancing state-of-the-art accuracy and robustness against transformations.

Findings

Achieves 42% improvement in IPT reconstruction accuracy.

Robust across unseen transformations and generative models.

Effectively identifies original images from near-duplicates.

Abstract

Near-duplicate images are often generated when applying repeated photometric and geometric transformations that produce imperceptible variants of the original image. Consequently, a deluge of near-duplicates can be circulated online posing copyright infringement concerns. The concerns are more severe when biometric data is altered through such nuanced transformations. In this work, we address the challenge of near-duplicate detection in face images by, firstly, identifying the original image from a set of near-duplicates and, secondly, deducing the relationship between the original image and the near-duplicates. We construct a tree-like structure, called an Image Phylogeny Tree (IPT) using a graph-theoretic approach to estimate the relationship, i.e., determine the sequence in which they have been generated. We further extend our method to create an ensemble of IPTs known as Image Phylogeny Forests (IPFs). We rigorously evaluate our method to demonstrate robustness across other modalities, unseen transformations by latest generative models and IPT configurations, thereby significantly advancing the state-of-the-art performance by 42% on IPF reconstruction accuracy.