# Detecting Near-Duplicate Face Images

arXiv: https://arxiv.org/abs/2408.07689 · HTML: https://mlsanity.com/paper/2408.07689/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 64 references that are in the map.
- First posted on arXiv 14 Aug 2024.

## Builds on (most cited first, 15 of 15)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering](https://mlsanity.com/paper/1606.09375/) (2016)
- [Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning](https://mlsanity.com/paper/1602.07261/) (2016)
- [Unsupervised Deep Embedding for Clustering Analysis](https://mlsanity.com/paper/1511.06335/) (2015)
- [Hypergraph Neural Networks](https://mlsanity.com/paper/1809.09401/) (2018)
- [Pivotal Tuning for Latent-based Editing of Real Images](https://mlsanity.com/paper/2106.05744/) (2021)
- [AttGAN: Facial Attribute Editing by Only Changing What You Want](https://mlsanity.com/paper/1711.10678/) (2017)
- [Deep Convolutional AutoEncoder-based Lossy Image Compression](https://mlsanity.com/paper/1804.09535/) (2018)
- [High Resolution Face Editing with Masked GAN Latent Code Optimization](https://mlsanity.com/paper/2103.11135/) (2021)
- [Image Provenance Analysis at Scale](https://mlsanity.com/paper/1801.06510/) (2018)
- [U-Phylogeny: Undirected Provenance Graph Construction in the Wild](https://mlsanity.com/paper/1705.11187/) (2017)
- [Provenance Filtering for Multimedia Phylogeny](https://mlsanity.com/paper/1706.00447/) (2017)
- [Comprehensive Dataset of Face Manipulations for Development and Evaluation of Forensic Tools](https://mlsanity.com/paper/2208.11776/) (2022)
- [Face Phylogeny Tree Using Basis Functions](https://mlsanity.com/paper/2002.09068/) (2020)
- [Mitigating the Impact of Attribute Editing on Face Recognition](https://mlsanity.com/paper/2403.08092/) (2024)

## Abstract (arXiv)

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.