# Noise-Coded Illumination for Forensic and Photometric Video Analysis

arXiv: https://arxiv.org/abs/2507.23002 · HTML: https://mlsanity.com/paper/2507.23002/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Watermarking generated content and models, one of 12 directions in LLM safety and security.
- Builds on 11 of its 68 references that are in the map.
- First posted on arXiv 30 Jul 2025.

## Placed in

- LLM safety and security / [Watermarking generated content and models](https://mlsanity.com/safety/watermarking-generated-content-and-models/)

## Builds on (most cited first, 11 of 11)

- [Fighting Fake News: Image Splice Detection via Learned Self-Consistency](https://mlsanity.com/paper/1805.04096/) (2018)
- [Self-Supervised Video Forensics by Audio-Visual Anomaly Detection](https://mlsanity.com/paper/2301.01767/) (2023)
- [Detecting Deep-Fake Videos from Appearance and Behavior](https://mlsanity.com/paper/2004.14491/) (2020)
- [CMUA-Watermark: A Cross-Model Universal Adversarial Watermark for Combating Deepfakes](https://mlsanity.com/paper/2105.10872/) (2021)
- [Detecting Photoshopped Faces by Scripting Photoshop](https://mlsanity.com/paper/1906.05856/) (2019)
- [Audio-Visual Person-of-Interest DeepFake Detection](https://mlsanity.com/paper/2204.03083/) (2022)
- [A Light Stage on Every Desk](https://mlsanity.com/paper/2105.08051/) (2021)
- [SRA: Fast Removal of General Multipath for ToF Sensors](https://mlsanity.com/paper/1403.5919/) (2014)
- [Content Authentication for Neural Imaging Pipelines: End-to-end Optimization of Photo Provenance in Complex Distribution Channels](https://mlsanity.com/paper/1812.01516/) (2018)
- [Totems: Physical Objects for Verifying Visual Integrity](https://mlsanity.com/paper/2209.13032/) (2022)
- [Image watermarking and fusion based on Fourier single-pixel imaging with weighed light source](https://mlsanity.com/paper/1910.06110/) (2019)

## Built on by (most cited first, 1 of 1)

- [Moir'e Video Authentication: A Physical Signature Against AI Video Generation](https://mlsanity.com/paper/2604.01654/) (2026)

## Abstract (arXiv)

The proliferation of advanced tools for manipulating video has led to an arms race, pitting those who wish to sow disinformation against those who want to detect and expose it. Unfortunately, time favors the ill-intentioned in this race, with fake videos growing increasingly difficult to distinguish from real ones. At the root of this trend is a fundamental advantage held by those manipulating media: equal access to a distribution of what we consider authentic (i.e., "natural") video. In this paper, we show how coding very subtle, noise-like modulations into the illumination of a scene can help combat this advantage by creating an information asymmetry that favors verification. Our approach effectively adds a temporal watermark to any video recorded under coded illumination. However, rather than encoding a specific message, this watermark encodes an image of the unmanipulated scene as it would appear lit only by the coded illumination. We show that even when an adversary knows that our technique is being used, creating a plausible coded fake video amounts to solving a second, more difficult version of the original adversarial content creation problem at an information disadvantage. This is a promising avenue for protecting high-stakes settings like public events and interviews, where the content on display is a likely target for manipulation, and while the illumination can be controlled, the cameras capturing video cannot.