# RepMix: Representation Mixing for Robust Attribution of Synthesized Images

arXiv: https://arxiv.org/abs/2207.02063 · HTML: https://mlsanity.com/paper/2207.02063/

## Key facts

- Cited by 39 papers in the map (data through 14 Oct 2026).
- Builds on 42 of its 65 references that are in the map.
- First posted on arXiv 5 Jul 2022.

## Builds on (most cited first, 30 of 42)

- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [Explaining and Harnessing Adversarial Examples](https://mlsanity.com/paper/1412.6572/) (2014)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [A Style-Based Generator Architecture for Generative Adversarial Networks](https://mlsanity.com/paper/1812.04948/) (2018)
- [mixup: Beyond Empirical Risk Minimization](https://mlsanity.com/paper/1710.09412/) (2017)
- [Deep Learning Face Attributes in the Wild](https://mlsanity.com/paper/1411.7766/) (2014)
- [Analyzing and Improving the Image Quality of StyleGAN](https://mlsanity.com/paper/1912.04958/) (2019)
- [Progressive Growing of GANs for Improved Quality, Stability, and Variation](https://mlsanity.com/paper/1710.10196/) (2017)
- [CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features](https://mlsanity.com/paper/1905.04899/) (2019)
- [Benchmarking Neural Network Robustness to Common Corruptions and Perturbations](https://mlsanity.com/paper/1903.12261/) (2019)
- [Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network](https://mlsanity.com/paper/1609.04802/) (2016)
- [The Many Faces of Robustness: A Critical Analysis of Out-of-Distribution Generalization](https://mlsanity.com/paper/2006.16241/) (2020)
- [Spectral Normalization for Generative Adversarial Networks](https://mlsanity.com/paper/1802.05957/) (2018)
- [LSUN: Construction of a Large-scale Image Dataset using Deep Learning with Humans in the Loop](https://mlsanity.com/paper/1506.03365/) (2015)
- [Semantic Image Synthesis with Spatially-Adaptive Normalization](https://mlsanity.com/paper/1903.07291/) (2019)
- [Demystifying MMD GANs](https://mlsanity.com/paper/1801.01401/) (2018)
- [Alias-Free Generative Adversarial Networks](https://mlsanity.com/paper/2106.12423/) (2021)
- [AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty](https://mlsanity.com/paper/1912.02781/) (2019)
- [CNN-generated images are surprisingly easy to spot... for now](https://mlsanity.com/paper/1912.11035/) (2019)
- [Generalizing to Unseen Domains: A Survey on Domain Generalization](https://mlsanity.com/paper/2103.03097/) (2021)
- [Generative Image Inpainting with Contextual Attention](https://mlsanity.com/paper/1801.07892/) (2018)
- [Leveraging Frequency Analysis for Deep Fake Image Recognition](https://mlsanity.com/paper/2003.08685/) (2020)
- [Detecting and Simulating Artifacts in GAN Fake Images](https://mlsanity.com/paper/1907.06515/) (2019)
- [Artificial Fingerprinting for Generative Models: Rooting Deepfake Attribution in Training Data](https://mlsanity.com/paper/2007.08457/) (2020)
- [Do GANs leave artificial fingerprints?](https://mlsanity.com/paper/1812.11842/) (2018)
- [HashNet: Deep Learning to Hash by Continuation](https://mlsanity.com/paper/1702.00758/) (2017)
- [The Cramer Distance as a Solution to Biased Wasserstein Gradients](https://mlsanity.com/paper/1705.10743/) (2017)
- [Invertible Conditional GANs for image editing](https://mlsanity.com/paper/1611.06355/) (2016)
- [Responsible Disclosure of Generative Models Using Scalable Fingerprinting](https://mlsanity.com/paper/2012.08726/) (2020)

## Built on by (most cited first, 30 of 39)

- [Hierarchical Fine-Grained Image Forgery Detection and Localization](https://mlsanity.com/paper/2303.17111/) (2023)
- [Online Detection of AI-Generated Images](https://mlsanity.com/paper/2310.15150/) (2023)
- [TrustMark: Universal Watermarking for Arbitrary Resolution Images](https://mlsanity.com/paper/2311.18297/) (2023)
- [Generalizable Synthetic Image Detection via Language-guided Contrastive Learning](https://mlsanity.com/paper/2305.13800/) (2023)
- [Evaluating Data Attribution for Text-to-Image Models](https://mlsanity.com/paper/2306.09345/) (2023)
- [PTW: Pivotal Tuning Watermarking for Pre-Trained Image Generators](https://mlsanity.com/paper/2304.07361/) (2023)
- [ArtiFact: A Large-Scale Dataset with Artificial and Factual Images for Generalizable and Robust Synthetic Image Detection](https://mlsanity.com/paper/2302.11970/) (2023)
- [Progressive Open Space Expansion for Open-Set Model Attribution](https://mlsanity.com/paper/2303.06877/) (2023)
- [Rethinking Image Editing Detection in the Era of Generative AI Revolution](https://mlsanity.com/paper/2311.17953/) (2023)
- [ProMark: Proactive Diffusion Watermarking for Causal Attribution](https://mlsanity.com/paper/2403.09914/) (2024)
- [Language-guided Hierarchical Fine-grained Image Forgery Detection and Localization](https://mlsanity.com/paper/2410.23556/) (2024)
- [Are CLIP features all you need for Universal Synthetic Image Origin Attribution?](https://mlsanity.com/paper/2408.09153/) (2024)
- [Synthetic Image Verification in the Era of Generative AI: What Works and What Isn't There Yet](https://mlsanity.com/paper/2405.00196/) (2024)
- [A Survey of Defenses Against AI-Generated Visual Media: Detection,Disruption, and Authentication](https://mlsanity.com/paper/2407.10575/) (2024)
- [Open Set Synthetic Image Source Attribution](https://mlsanity.com/paper/2308.11557/) (2023)
- [Forensic Self-Descriptions Are All You Need for Zero-Shot Detection, Open-Set Source Attribution, and Clustering of AI-generated Images](https://mlsanity.com/paper/2503.21003/) (2025)
- [What can Discriminator do? Towards Box-free Ownership Verification of Generative Adversarial Network](https://mlsanity.com/paper/2307.15860/) (2023)
- [Detecting Origin Attribution for Text-to-Image Diffusion Models](https://mlsanity.com/paper/2403.19653/) (2024)
- [VADER: Video Alignment Differencing and Retrieval](https://mlsanity.com/paper/2303.13193/) (2023)
- [BOSC: A Backdoor-based Framework for Open Set Synthetic Image Attribution](https://mlsanity.com/paper/2405.11491/) (2024)
- [Few-Shot Synthetic Image Attribution: Identifying Unseen Generators with Limited Samples](https://mlsanity.com/paper/2509.25682/) (2025)
- [Model Synthesis for Zero-Shot Model Attribution](https://mlsanity.com/paper/2307.15977/) (2023)
- [Are handcrafted filters helpful for attributing AI-generated images?](https://mlsanity.com/paper/2407.14570/) (2024)
- [Learning Counterfactually Decoupled Attention for Open-World Model Attribution](https://mlsanity.com/paper/2506.23074/) (2025)
- [Passive Deepfake Detection Across Multi-modalities: A Comprehensive Survey](https://mlsanity.com/paper/2411.17911/) (2024)
- [Regeneration Based Training-free Attribution of Fake Images Generated by Text-to-Image Generative Models](https://mlsanity.com/paper/2403.01489/) (2024)
- [Attribution as Retrieval: Model-Agnostic AI-Generated Image Attribution](https://mlsanity.com/paper/2603.10583/) (2026)
- [IncreFA: Breaking the Static Wall of Generative Model Attribution](https://mlsanity.com/paper/2604.17736/) (2026)
- [Who Made This? Fake Detection and Source Attribution with Diffusion Features](https://mlsanity.com/paper/2510.27602/) (2025)
- [Autonomous and Self-Adapting System for Synthetic Media Detection and Attribution](https://mlsanity.com/paper/2504.03615/) (2025)

## Abstract (arXiv)

Rapid advances in Generative Adversarial Networks (GANs) raise new challenges for image attribution; detecting whether an image is synthetic and, if so, determining which GAN architecture created it. Uniquely, we present a solution to this task capable of 1) matching images invariant to their semantic content; 2) robust to benign transformations (changes in quality, resolution, shape, etc.) commonly encountered as images are re-shared online. In order to formalize our research, a challenging benchmark, Attribution88, is collected for robust and practical image attribution. We then propose RepMix, our GAN fingerprinting technique based on representation mixing and a novel loss. We validate its capability of tracing the provenance of GAN-generated images invariant to the semantic content of the image and also robust to perturbations. We show our approach improves significantly from existing GAN fingerprinting works on both semantic generalization and robustness. Data and code are available at https://github.com/TuBui/image_attribution.