# UVCG: Leveraging Temporal Consistency for Universal Video Protection

arXiv: https://arxiv.org/abs/2411.17746 · HTML: https://mlsanity.com/paper/2411.17746/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 35 references that are in the map.
- First posted on arXiv 25 Nov 2024.

## Builds on (most cited first, 28 of 28)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [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)
- [Towards Deep Learning Models Resistant to Adversarial Attacks](https://mlsanity.com/paper/1706.06083/) (2017)
- [BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models](https://mlsanity.com/paper/2301.12597/) (2023)
- [InstructPix2Pix: Learning to Follow Image Editing Instructions](https://mlsanity.com/paper/2211.09800/) (2022)
- [Tune-A-Video: One-Shot Tuning of Image Diffusion Models for Text-to-Video Generation](https://mlsanity.com/paper/2212.11565/) (2022)
- [The 2017 DAVIS Challenge on Video Object Segmentation](https://mlsanity.com/paper/1704.00675/) (2017)
- [Text2Video-Zero: Text-to-Image Diffusion Models are Zero-Shot Video Generators](https://mlsanity.com/paper/2303.13439/) (2023)
- [Structure and Content-Guided Video Synthesis with Diffusion Models](https://mlsanity.com/paper/2302.03011/) (2023)
- [Delving into Transferable Adversarial Examples and Black-box Attacks](https://mlsanity.com/paper/1611.02770/) (2016)
- [FateZero: Fusing Attentions for Zero-shot Text-based Video Editing](https://mlsanity.com/paper/2303.09535/) (2023)
- [Transferability in Machine Learning: from Phenomena to Black-Box Attacks using Adversarial Samples](https://mlsanity.com/paper/1605.07277/) (2016)
- [TokenFlow: Consistent Diffusion Features for Consistent Video Editing](https://mlsanity.com/paper/2307.10373/) (2023)
- [Video-P2P: Video Editing with Cross-attention Control](https://mlsanity.com/paper/2303.04761/) (2023)
- [Glaze: Protecting Artists from Style Mimicry by Text-to-Image Models](https://mlsanity.com/paper/2302.04222/) (2023)
- [Adversarial Example Does Good: Preventing Painting Imitation from Diffusion Models via Adversarial Examples](https://mlsanity.com/paper/2302.04578/) (2023)
- [Raising the Cost of Malicious AI-Powered Image Editing](https://mlsanity.com/paper/2302.06588/) (2023)
- [SINE: SINgle Image Editing with Text-to-Image Diffusion Models](https://mlsanity.com/paper/2212.04489/) (2022)
- [Mist: Towards Improved Adversarial Examples for Diffusion Models](https://mlsanity.com/paper/2305.12683/) (2023)
- [CRoSS: Diffusion Model Makes Controllable, Robust and Secure Image Steganography](https://mlsanity.com/paper/2305.16936/) (2023)
- [DiffAttack: Evasion Attacks Against Diffusion-Based Adversarial Purification](https://mlsanity.com/paper/2311.16124/) (2023)
- [Diff-Privacy: Diffusion-based Face Privacy Protection](https://mlsanity.com/paper/2309.05330/) (2023)
- [iEdit: Localised Text-guided Image Editing with Weak Supervision](https://mlsanity.com/paper/2305.05947/) (2023)
- [PRIME: Protect Your Videos From Malicious Editing](https://mlsanity.com/paper/2402.01239/) (2024)
- [Inter-frame Accelerate Attack against Video Interpolation Models](https://mlsanity.com/paper/2305.06540/) (2023)

## Built on by (most cited first, 2 of 2)

- [VideoGuard: Protecting Video Content from Unauthorized Editing](https://mlsanity.com/paper/2508.03480/) (2025)
- [A Survey on Adversarial Attacks and Defenses for Diffusion Models Across Multiple Modalities](https://mlsanity.com/paper/2609.05503/) (2026)

## Abstract (arXiv)

The security risks of AI-driven video editing have garnered significant attention. Although recent studies indicate that adding perturbations to images can protect them from malicious edits, directly applying image-based methods to perturb each frame in a video becomes ineffective, as video editing techniques leverage the consistency of inter-frame information to restore individually perturbed content. To address this challenge, we leverage the temporal consistency of video content to propose a straightforward and efficient, yet highly effective and broadly applicable approach, Universal Video Consistency Guard (UVCG). UVCG embeds the content of another video(target video) within a protected video by introducing continuous, imperceptible perturbations which has the ability to force the encoder of editing models to map continuous inputs to misaligned continuous outputs, thereby inhibiting the generation of videos consistent with the intended textual prompts. Additionally leveraging similarity in perturbations between adjacent frames, we improve the computational efficiency of perturbation generation by employing a perturbation-reuse strategy. We applied UVCG across various versions of Latent Diffusion Models (LDM) and assessed its effectiveness and generalizability across multiple LDM-based editing pipelines. The results confirm the effectiveness, transferability, and efficiency of our approach in safeguarding video content from unauthorized modifications.