# Can Tainted Pixels Expose Deepfake Videos?

arXiv: https://arxiv.org/abs/2608.27492 · HTML: https://mlsanity.com/paper/2608.27492/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 29 of its 52 references that are in the map.
- First posted on arXiv 26 Aug 2026.

## Builds on (most cited first, 29 of 29)

- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [FaceForensics++: Learning to Detect Manipulated Facial Images](https://mlsanity.com/paper/1901.08971/) (2019)
- [Celeb-DF: A Large-scale Challenging Dataset for DeepFake Forensics](https://mlsanity.com/paper/1909.12962/) (2019)
- [The Creation and Detection of Deepfakes: A Survey](https://mlsanity.com/paper/2004.11138/) (2020)
- [DeepFakes and Beyond: A Survey of Face Manipulation and Fake Detection](https://mlsanity.com/paper/2001.00179/) (2020)
- [Transcending Forgery Specificity with Latent Space Augmentation for Generalizable Deepfake Detection](https://mlsanity.com/paper/2311.11278/) (2023)
- [The spread of low-credibility content by social bots](https://mlsanity.com/paper/1707.07592/) (2017)
- [Disrupting Deepfakes: Adversarial Attacks Against Conditional Image Translation Networks and Facial Manipulation Systems](https://mlsanity.com/paper/2003.01279/) (2020)
- [Deepfake Detection by Human Crowds, Machines, and Machine-informed Crowds](https://mlsanity.com/paper/2105.06496/) (2021)
- [CMUA-Watermark: A Cross-Model Universal Adversarial Watermark for Combating Deepfakes](https://mlsanity.com/paper/2105.10872/) (2021)
- [FakeTagger: Robust Safeguards against DeepFake Dissemination via Provenance Tracking](https://mlsanity.com/paper/2009.09869/) (2020)
- [FLOAT: Generative Motion Latent Flow Matching for Audio-driven Talking Portrait](https://mlsanity.com/paper/2412.01064/) (2024)
- [Anti-Forgery: Towards a Stealthy and Robust DeepFake Disruption Attack via Adversarial Perceptual-aware Perturbations](https://mlsanity.com/paper/2206.00477/) (2022)
- [Realistic and Efficient Face Swapping: A Unified Approach with Diffusion Models](https://mlsanity.com/paper/2409.07269/) (2024)
- [MuseTalk: Real-Time High-Fidelity Video Dubbing via Spatio-Temporal Sampling](https://mlsanity.com/paper/2410.10122/) (2024)
- [TAFIM: Targeted Adversarial Attacks against Facial Image Manipulations](https://mlsanity.com/paper/2112.09151/) (2021)
- [LampMark: Proactive Deepfake Detection via Training-Free Landmark Perceptual Watermarks](https://mlsanity.com/paper/2411.17209/) (2024)
- [Face Anonymization Made Simple](https://mlsanity.com/paper/2411.00762/) (2024)
- [Audio-visual Controlled Video Diffusion with Masked Selective State Spaces Modeling for Natural Talking Head Generation](https://mlsanity.com/paper/2504.02542/) (2025)
- [Human Detection of Political Speech Deepfakes across Transcripts, Audio, and Video](https://mlsanity.com/paper/2202.12883/) (2022)
- [Are Watermarks Bugs for Deepfake Detectors? Rethinking Proactive Forensics](https://mlsanity.com/paper/2404.17867/) (2024)
- [DynamicFace: High-Quality and Consistent Face Swapping for Image and Video using Composable 3D Facial Priors](https://mlsanity.com/paper/2501.08553/) (2025)
- [FaceShield: Defending Facial Image against Deepfake Threats](https://mlsanity.com/paper/2412.09921/) (2024)
- [NullSwap: Proactive Identity Cloaking Against Deepfake Face Swapping](https://mlsanity.com/paper/2503.18678/) (2025)
- [DeepShield: Fortifying Deepfake Video Detection with Local and Global Forgery Analysis](https://mlsanity.com/paper/2510.25237/) (2025)
- [Undercover Deepfakes: Detecting Fake Segments in Videos](https://mlsanity.com/paper/2305.06564/) (2023)
- [Hiding Faces in Plain Sight: Defending DeepFakes by Disrupting Face Detection](https://mlsanity.com/paper/2412.01101/) (2024)
- [FaceSwapGuard: Safeguarding Facial Privacy from DeepFake Threats through Identity Obfuscation](https://mlsanity.com/paper/2502.10801/) (2025)
- [Towards Generalized Proactive Defense against Face Swapping with Contour-Hybrid Watermark](https://mlsanity.com/paper/2505.19081/) (2025)

## Abstract (arXiv)

Publicly-acceesible face-manipulation tools have made deepfake creation accessible to non-expert users. Against these, existing defenses are mostly post-hoc, detecting only after forgery has occurred, and operating on still images rather than videos. Research is lacking in i) the proactive protection of published facial videos against black-box manipulation tools, and in (ii) understanding its perceptual effect on human viewers. We introduce TaintedPixels, a proactive video-protection method built around an asymmetric visibility trade-off: the embedded watermark should remain inconspicuous in the published video but become obvious once a downstream tool manipulates the video. TaintedPixels injects structured periodic perturbations into the blue channel of facial regions and refines them under stripe-visibility, color-cast, and video-level LPIPS budgets, with lightweight motion-adaptive deployment. We believe TaintedPixels is the first proactive defense designed specifically against black-box manipulation tools rather than image-level pipelines or specific surrogate generators. Across three publicly available off-the-shelf video manipulation tools and two off-the-shelf detectors, TaintedPixels attains the highest forgery fake rate while keeping perturbations small (LPIPS = 0.0042). Our non-expert human study, conducted on a diverse set of 300 video stimuli spanning different lighting conditions, backgrounds, and skin tones, shows that protected source videos draw a 3.26% suspicion rate, while forgeries from protected sources are identified as fake much more often than forgeries from unprotected sources (90.72% vs. 56.71%). This validates the effectiveness of TaintedPixels.