# VideoGuard: Protecting Video Content from Unauthorized Editing arXiv: https://arxiv.org/abs/2508.03480 ยท HTML: https://mlsanity.com/paper/2508.03480/ ## Key facts - Cited by 3 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 31 of its 40 references that are in the map. - First posted on arXiv 5 Aug 2025. ## Placed in - Diffusion models / [Robustness, privacy and adversarial aspects](https://mlsanity.com/diffusion/robustness-privacy-and-adversarial-aspects/) ## Builds on (most cited first, 30 of 31) - [Denoising Diffusion Implicit Models](https://mlsanity.com/paper/2010.02502/) (2020) - [Hierarchical Text-Conditional Image Generation with CLIP Latents](https://mlsanity.com/paper/2204.06125/) (2022) - [Towards Deep Learning Models Resistant to Adversarial Attacks](https://mlsanity.com/paper/1706.06083/) (2017) - [Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding](https://mlsanity.com/paper/2205.11487/) (2022) - [GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models](https://mlsanity.com/paper/2112.10741/) (2021) - [DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generation](https://mlsanity.com/paper/2208.12242/) (2022) - [Prompt-to-Prompt Image Editing with Cross Attention Control](https://mlsanity.com/paper/2208.01626/) (2022) - [VBench: Comprehensive Benchmark Suite for Video Generative Models](https://mlsanity.com/paper/2311.17982/) (2023) - [Make-A-Video: Text-to-Video Generation without Text-Video Data](https://mlsanity.com/paper/2209.14792/) (2022) - [Diffusion Models in Vision: A Survey](https://mlsanity.com/paper/2209.04747/) (2022) - [Tune-A-Video: One-Shot Tuning of Image Diffusion Models for Text-to-Video Generation](https://mlsanity.com/paper/2212.11565/) (2022) - [DiffusionCLIP: Text-Guided Diffusion Models for Robust Image Manipulation](https://mlsanity.com/paper/2110.02711/) (2021) - [FateZero: Fusing Attentions for Zero-shot Text-based Video Editing](https://mlsanity.com/paper/2303.09535/) (2023) - [Video-P2P: Video Editing with Cross-attention Control](https://mlsanity.com/paper/2303.04761/) (2023) - [MotionDirector: Motion Customization of Text-to-Video Diffusion Models](https://mlsanity.com/paper/2310.08465/) (2023) - [Raising the Cost of Malicious AI-Powered Image Editing](https://mlsanity.com/paper/2302.06588/) (2023) - [TrojDiff: Trojan Attacks on Diffusion Models with Diverse Targets](https://mlsanity.com/paper/2303.05762/) (2023) - [VMC: Video Motion Customization using Temporal Attention Adaption for Text-to-Video Diffusion Models](https://mlsanity.com/paper/2312.00845/) (2023) - [MagicEdit: High-Fidelity and Temporally Coherent Video Editing](https://mlsanity.com/paper/2308.14749/) (2023) - [Prompt Stealing Attacks Against Text-to-Image Generation Models](https://mlsanity.com/paper/2302.09923/) (2023) - [CRoSS: Diffusion Model Makes Controllable, Robust and Secure Image Steganography](https://mlsanity.com/paper/2305.16936/) (2023) - [VideoShield: Regulating Diffusion-based Video Generation Models via Watermarking](https://mlsanity.com/paper/2501.14195/) (2025) - [LoRA-Edit: Controllable First-Frame-Guided Video Editing via Mask-Aware LoRA Fine-Tuning](https://mlsanity.com/paper/2506.10082/) (2025) - [Diff-Privacy: Diffusion-based Face Privacy Protection](https://mlsanity.com/paper/2309.05330/) (2023) - [Intellectual Property Protection of Diffusion Models via the Watermark Diffusion Process](https://mlsanity.com/paper/2306.03436/) (2023) - [VideoMark: A Distortion-Free Robust Watermarking Framework for Video Diffusion Models](https://mlsanity.com/paper/2504.16359/) (2025) - [PRIME: Protect Your Videos From Malicious Editing](https://mlsanity.com/paper/2402.01239/) (2024) - [LVMark: Robust Watermark for Latent Video Diffusion Models](https://mlsanity.com/paper/2412.09122/) (2024) - [Video Watermarking: Safeguarding Your Video from (Unauthorized) Annotations by Video-based LLMs](https://mlsanity.com/paper/2407.02411/) (2024) - [Protecting Your Video Content: Disrupting Automated Video-based LLM Annotations](https://mlsanity.com/paper/2503.21824/) (2025) ## Built on by (most cited first, 3 of 3) - [Delving into the Temporal Challenges of Unified Video Protection Against Image-to-Video and Fine-Tuning-based Customization](https://mlsanity.com/paper/2607.13336/) (2026) - [Pulling The REINS: Training-Free Safety Alignment of Video Diffusion Models via Representation Steering](https://mlsanity.com/paper/2606.17257/) (2026) - [A Survey on Adversarial Attacks and Defenses for Diffusion Models Across Multiple Modalities](https://mlsanity.com/paper/2609.05503/) (2026) ## Abstract (arXiv) With the rapid development of generative technology, current generative models can generate high-fidelity digital content and edit it in a controlled manner. However, there is a risk that malicious individuals might misuse these capabilities for misleading activities. Although existing research has attempted to shield photographic images from being manipulated by generative models, there remains a significant disparity in the protection offered to video content editing. To bridge the gap, we propose a protection method named VideoGuard, which can effectively protect videos from unauthorized malicious editing. This protection is achieved through the subtle introduction of nearly unnoticeable perturbations that interfere with the functioning of the intended generative diffusion models. Due to the redundancy between video frames, and inter-frame attention mechanism in video diffusion models, simply applying image-based protection methods separately to every video frame can not shield video from unauthorized editing. To tackle the above challenge, we adopt joint frame optimization, treating all video frames as an optimization entity. Furthermore, we extract video motion information and fuse it into optimization objectives. Thus, these alterations can effectively force the models to produce outputs that are implausible and inconsistent. We provide a pipeline to optimize this perturbation. Finally, we use both objective metrics and subjective metrics to demonstrate the efficacy of our method, and the results show that the protection performance of VideoGuard is superior to all the baseline methods.