# Diffusion models meet image counter-forensics

arXiv: https://arxiv.org/abs/2311.13629 · HTML: https://mlsanity.com/paper/2311.13629/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Placed in Robustness, privacy and adversarial aspects, one of 14 directions in Diffusion models.
- Builds on 24 of its 62 references that are in the map.
- First posted on arXiv 22 Nov 2023.

## Placed in

- Diffusion models / [Robustness, privacy and adversarial aspects](https://mlsanity.com/diffusion/robustness-privacy-and-adversarial-aspects/)

## Builds on (most cited first, 24 of 24)

- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [Score-Based Generative Modeling through Stochastic Differential Equations](https://mlsanity.com/paper/2011.13456/) (2020)
- [Deep Unsupervised Learning using Nonequilibrium Thermodynamics](https://mlsanity.com/paper/1503.03585/) (2015)
- [SDEdit: Guided Image Synthesis and Editing with Stochastic Differential Equations](https://mlsanity.com/paper/2108.01073/) (2021)
- [DiffWave: A Versatile Diffusion Model for Audio Synthesis](https://mlsanity.com/paper/2009.09761/) (2020)
- [Diffusion Probabilistic Models for 3D Point Cloud Generation](https://mlsanity.com/paper/2103.01458/) (2021)
- [Diffusion Models for Adversarial Purification](https://mlsanity.com/paper/2205.07460/) (2022)
- [Defense-GAN: Protecting Classifiers Against Adversarial Attacks Using Generative Models](https://mlsanity.com/paper/1805.06605/) (2018)
- [On the detection of synthetic images generated by diffusion models](https://mlsanity.com/paper/2211.00680/) (2022)
- [TruFor: Leveraging all-round clues for trustworthy image forgery detection and localization](https://mlsanity.com/paper/2212.10957/) (2022)
- [Noiseprint: a CNN-based camera model fingerprint](https://mlsanity.com/paper/1808.08396/) (2018)
- [Guided Diffusion Model for Adversarial Purification](https://mlsanity.com/paper/2205.14969/) (2022)
- [Learning JPEG Compression Artifacts for Image Manipulation Detection and Localization](https://mlsanity.com/paper/2108.12947/) (2021)
- [Robust Evaluation of Diffusion-Based Adversarial Purification](https://mlsanity.com/paper/2303.09051/) (2023)
- [Guided Diffusion Model for Adversarial Purification from Random Noise](https://mlsanity.com/paper/2206.10875/) (2022)
- [DensePure: Understanding Diffusion Models towards Adversarial Robustness](https://mlsanity.com/paper/2211.00322/) (2022)
- [Defending against Adversarial Audio via Diffusion Model](https://mlsanity.com/paper/2303.01507/) (2023)
- [PointDP: Diffusion-driven Purification against Adversarial Attacks on 3D Point Cloud Recognition](https://mlsanity.com/paper/2208.09801/) (2022)
- [SpoC: Spoofing Camera Fingerprints](https://mlsanity.com/paper/1911.12069/) (2019)
- [Comprint: Image Forgery Detection and Localization using Compression Fingerprints](https://mlsanity.com/paper/2210.02227/) (2022)
- [Camera Trace Erasing](https://mlsanity.com/paper/2003.06951/) (2020)
- [Analysis of adversarial attacks against CNN-based image forgery detectors](https://mlsanity.com/paper/1808.08426/) (2018)
- [Denoising Diffusion Probabilistic Models as a Defense against Adversarial Attacks](https://mlsanity.com/paper/2301.06871/) (2023)

## Built on by (most cited first, 6 of 6)

- [TGIF: Text-Guided Inpainting Forgery Dataset](https://mlsanity.com/paper/2407.11566/) (2024)
- [EvoGuard: An Extensible Agentic RL-based Framework for Practical and Evolving AI-Generated Image Detection](https://mlsanity.com/paper/2603.17343/) (2026)
- [Hiding Local Manipulations on SAR Images: a Counter-Forensic Attack](https://mlsanity.com/paper/2407.07041/) (2024)
- [Deep Image Restoration For Image Anti-Forensics](https://mlsanity.com/paper/2405.02751/) (2024)
- [UGD-IML: A Unified Generative Diffusion-based Framework for Constrained and Unconstrained Image Manipulation Localization](https://mlsanity.com/paper/2508.06101/) (2025)
- [Attack-Aware Deepfake Detection under Counter-Forensic Manipulations](https://mlsanity.com/paper/2512.22303/) (2025)

## Abstract (arXiv)

From its acquisition in the camera sensors to its storage, different operations are performed to generate the final image. This pipeline imprints specific traces into the image to form a natural watermark. Tampering with an image disturbs these traces; these disruptions are clues that are used by most methods to detect and locate forgeries. In this article, we assess the capabilities of diffusion models to erase the traces left by forgers and, therefore, deceive forensics methods. Such an approach has been recently introduced for adversarial purification, achieving significant performance. We show that diffusion purification methods are well suited for counter-forensics tasks. Such approaches outperform already existing counter-forensics techniques both in deceiving forensics methods and in preserving the natural look of the purified images. The source code is publicly available at https://github.com/mtailanian/diff-cf.