Delving into the Temporal Challenges of Unified Video Protection Against Image-to-Video and Fine-Tuning-based Customization

arXiv:2607.13336 · cs.CV, cs.CR, cs.LG · Submitted 2026-07-14 · Read on arXiv

Yuxin Huang, Ziming Hong, Mingming Gong, Wanyu Wang, Jing Zhang, Tongliang Liu

cs.CV, cs.CR, cs.LG

Submitted: 2026-07-14

Comments: This work provides a basis for the ECCV 2026 LifeGenIP Challenge on Unlearnable Videos against Diffusion-based Customization. Challenge page: https://lifegenip.cc/competition. Evaluation code: https://github.com/tmllab/ECCV26_LifeGenIP_starting_kit. Project page: https://saythe17.github.io/TC-UAP/

Code: https://github.com/tmllab/ECCV26_LifeGenIP_starting_kit

Project page: https://saythe17.github.io/TC-UAP

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Diffusion Model — 21× in this paper · explained in 8 episodes
• Consistency — 19× in this paper · explained in 3 episodes
• Diffusion Models — 15× in this paper · explained in 6 episodes
• Robustness — 14× in this paper · explained in 5 episodes
• Temporal consistency — 10× in this paper · explained in 2 episodes
• Latent Representation — 8× in this paper · explained in 2 episodes
• Fine-Tuning — 7× in this paper · explained in 5 episodes
• Latent Representations — 6× in this paper · explained in 3 episodes
• Generative Model — 5× in this paper · explained in 2 episodes
• Generative Models — 5× in this paper · explained in 2 episodes
• Ablation — 4× in this paper · explained in 1 episode
• Benchmark — 4× in this paper · explained in 2 episodes
• Latent Space — 4× in this paper · explained in 7 episodes
• Adaptation — 3× in this paper · explained in 1 episode
• Face Detection Failure Rate — 3× in this paper
• Fidelity — 3× in this paper · explained in 2 episodes
• LoRA Fine-Tuning — 3× in this paper · explained in 3 episodes
• Video Multi-Method Assessment Fusion — 3× in this paper
• 3D Gaussian Splatting — 2× in this paper · explained in 2 episodes
• Ablation study — 2× in this paper · explained in 1 episode
• Adam — 2× in this paper · explained in 2 episodes
• Alignment — 2× in this paper · explained in 1 episode
• Cosine Similarity — 2× in this paper · explained in 3 episodes
• Embeddings — 2× in this paper · explained in 2 episodes
• Foundation Model — 2× in this paper · explained in 3 episodes
• Gaussian Splatting — 2× in this paper · explained in 4 episodes
• Generalization — 2× in this paper · explained in 13 episodes
• Latent Diffusion Model — 2× in this paper · explained in 2 episodes
• Latent Diffusion Models — 2× in this paper · explained in 2 episodes
• LoRA Adapters — 2× in this paper · explained in 3 episodes
• Projected Gradient Descent — 2× in this paper
• Transformer — 2× in this paper · explained in 2 episodes
• Ablation Studies — 1× in this paper · explained in 1 episode
• Adaptive Safety — 1× in this paper · explained in 1 episode
• Adversarial Attacks — 1× in this paper · explained in 1 episode
• Adversarial Examples — 1× in this paper · explained in 2 episodes
• Adversarial Noise — 1× in this paper · explained in 1 episode
• Aggregation — 1× in this paper · explained in 1 episode
• Benchmarking — 1× in this paper · explained in 3 episodes
• Cooperation — 1× in this paper · explained in 2 episodes
• Decode — 1× in this paper · explained in 2 episodes
• Failure Modes — 1× in this paper · explained in 3 episodes
• Flow Matching — 1× in this paper · explained in 8 episodes
• Foundation Models — 1× in this paper · explained in 7 episodes
• Latent Diffusion Models (LDMs) — 1× in this paper · explained in 2 episodes
• Low-Rank Adaptation (LoRA) — 1× in this paper · explained in 9 episodes
• Meta-learning — 1× in this paper · explained in 2 episodes
• Optimal Transport — 1× in this paper · explained in 5 episodes
• Personalization — 1× in this paper · explained in 2 episodes
• Redundancy — 1× in this paper · explained in 2 episodes
• Reinforcement Learning — 1× in this paper · explained in 4 episodes
• Transferability — 1× in this paper · explained in 4 episodes
• Unified Framework — 1× in this paper · explained in 5 episodes
• Verification — 1× in this paper · explained in 2 episodes

Sources

• Stable Video Diffusion: Scaling Latent Video Diffusion Models to Large Datasets
• VideoGuard: Protecting Video Content from Unauthorized Editing
• TalkVid: A Large-Scale Diversified Dataset for Audio-Driven Talking Head Synthesis
• Identity as Presence: Towards Appearance and Voice Personalized Joint Audio-Video Generation
• Vid-Freeze: Protecting Images from Malicious Image-to-Video Generation via Temporal Freezing
• Token Buncher: Shielding LLMs from Harmful Reinforcement Learning Fine-Tuning
• DreamID-Omni: Unified Framework for Controllable Human-Centric Audio-Video Generation
• LTX-Video: Realtime Video Latent Diffusion
• LTX-2: Efficient Joint Audio-Visual Foundation Model
• ID-Animator: Zero-Shot Identity-Preserving Human Video Generation
• AdLift: Lifting Adversarial Perturbations to Safeguard 3D Gaussian Splatting Assets Against Instruction-Driven Editing
• Toward Robust Non-Transferable Learning: A Survey and Benchmark
• HunyuanVideo: A Systematic Framework For Large Video Generative Models
• PRIME: Protect Your Videos From Malicious Editing
• IPV-Bench: Benchmarking Image Protection Methods under Diverse Image-to-Video Generation Scenarios
• Rethinking Data Protection in the (Generative) Artificial Intelligence Era
• Mist: Towards Improved Adversarial Examples for Diffusion Models
• Adversarial Example Does Good: Preventing Painting Imitation from Diffusion Models via Adversarial Examples
• SubFlow: Sub-mode Conditioned Flow Matching for Diverse One-Step Generation
• Immune2V: Image Immunization Against Dual-Stream Image-to-Video Generation

Related papers

• Loss Knows Best: Detecting Annotation Errors in Videos via Loss Trajectories
• AnchorWeave: World-Consistent Video Generation with Retrieved Local Spatial Memories
• Benchmarking the Robustness of Foundation Models for Mammography under Domain Shift
• MambaX-Net: Dual-Input Mamba-Enhanced Cross-Attention Network for Longitudinal MRI Segmentation
• TeleOCR: Navigating Document Parsing Across Digital and Camera-Captured Documents
• A Survey on Efficient Vision-Language-Action Models