KVeil: First-Place Solution to ECCV 2026 LifeGenIP Challenge on Unlearnable Videos

Boheng Li; Yueming Xing; Yiming Li; Jie Zhang; Han Qiu; Tianwei Zhang

cs.CVOct 9, 2026

KVeil: First-Place Solution to ECCV 2026 LifeGenIP Challenge on Unlearnable Videos

Authors: Boheng Li, Yueming Xing, Yiming Li, Jie Zhang, Han Qiu, Tianwei Zhang

Organizations: Nanyang Technological University, Singapore · Wuhan University, China · SiliconProspect AI · Tsinghua University, China

Abstract

The rapid development of personalized video generation has raised serious concerns over identity privacy, as a few facial videos of a person now suffice to reproduce their identity in synthetic videos. This report presents the first-place solutions to both phases of the ECCV 2026 LifeGenIP Challenge on Unlearnable Videos, which aims to protect facial videos from unauthorized customization under a limited and imperceptible perturbation budget. For Phase 1, we introduce Reference Capacity Collapse, a pair of complementary losses that degrade the information carried by the reference condition while keeping subsequent video representations dependent on it, together with a Motion-Guided EOT mechanism that improves robustness across unknown inference conditions. Phase 2 further considers tuning-based customization and unseen generative models. To address both threats within a single perturbation, we design a two-layer framework that protects the facial and background regions separately, constructing structure-aligned perturbations from a synthetic target identity in the former and strengthening the white-box disruption of Phase 1 in the latter. Our method achieves nearly perfect protection under white-box reference-based customization, and remains effective under other customization paradigms, various models, and temporal attacks. Both solutions are built upon our recent work LETHE. We hope our solutions can inspire future work on protecting facial videos against unauthorized customization. Our code is available at https://github.com/AntigoneRandy/KVeil.

Figures & tables

Panel of Table 1 — Table 1: Leaderboard results of Phase 1 in the LifeGenIP Challenge. Higher values indicate better performance for all metrics. Invis., Eff., and Rob. refer to invisibility, effectiveness, and robustness, respectively.

Panel of Table 2 — Table 2: Leaderboard results of Phase 2 in the LifeGenIP Challenge. Higher values indicate better performance for all metrics. Invis., Eff., Rob., and Trans. refer to invisibility, effectiveness, robustness, and transferability, respectively.

Figure 1: Qualitative results of Phase 1 under reference-based customization.

Attack Pathways	Track	Metrics	Phase 1	Phase 2
	Invisibility	Invis.	$\checkmark$	$\checkmark$
Reference- based	Effectiveness	FIP	$\checkmark$ Wan2.2-TI2V-5B	$\checkmark$ Wan2.2-TI2V-5B
	Robustness	FIP	$\checkmark$ Neighbor-frame interpolation $\checkmark$ 7-frame temporal averaging $\checkmark$ H.264 re-compression	$\checkmark$ 7-frame temporal averaging + H.264 re-compression $\checkmark$ VideoPure purification
	Transferability	FIP	–	$\checkmark$ HunyuanVideo-1.5 $\checkmark$ Kling 3.0
Tuning- based	Effectiveness	FIP	–	$\checkmark$ Wan2.2-TI2V-5B
	Robustness	FIP	–	$\checkmark$ 5-frame temporal averaging + temporal reversal $\checkmark$ H.264 re-compression

Table 3: Evaluation protocols in the two phases. Each check mark refers to one evaluated setting. FIP combines face, image quality, and prompt following scores. Invis. refers to invisibility score. Gray text indicates black-box evaluation settings.

Figure 2: Landmark-guided propagation of the face-layer perturbation across different poses.

Customization Setting	Face $\uparrow$	IQ $\uparrow$	PF $\uparrow$
Wan2.2 I2V (Clean Ref.)	16.79	25.39	12.33
Wan2.2 I2V (Protected Ref.)	82.66	27.59	35.51
HunyuanVideo-1.5 I2V (Protected Ref.)	34.40	26.08	26.53
Wan2.2 LoRA-1500 (Tune)	51.09	37.11	17.64

Table 4: Quantitative results of Phase 2.

Figure 3: Ablation of the Phase 1 protection objectives, adding K-attract, and auxiliary objectives to V-capacity collapse.

Target	Hunyuan	Wan2.2 LoRA
T0 (in use)	66.86	93.12
T1	61.72	75.81
T2	60.56	87.27
T3	60.55	98.28
T4	62.49	83.60
T5	58.90	78.83

Table 5: Comparison of different targets on HunyuanVideo-1.5 and Wan2.2 LoRA finetuning 1500 steps. Results report the Face Score under the apple prompt.

Figure 4: Candidate target identity images used for target selection

Unlearnable Examples

Unlearnable examples use data perturbations to prevent machine-learning models from learning protected images, text, graphs, or other data.

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Jul 14, 2026J/K move · Enter open · S save

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

Yuxin Huang, Ziming Hong, Mingming Gong, Wanyu Wang+2

1Sydney AI Centre, The University of Sydney · University of Melbourne · 5Mohamed bin Zayed University of Artificial Intelligence+2

Video Diffusion Models Adversarial Robustness Adversarial Perturbations

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Recent diffusion-based video generation models have enabled high-quality personalized video customization through both tuning-based pipelines, which fine-tune a video diffusion model, and reference-based pipelines such as image-to-video generation. However, these capabilities raise serious concerns about personal privacy, identity ownership and intellectual property protection. Existing anti-customization works focus on protecting images, while protection for videos against both reference- and tuning-based customization remains largely underexplored. Protecting videos in this setting raises three challenges: (i) Image-level perturbations, optimized frame by frame, cannot survive temporal compression by 3D video VAE. (ii) A video-level perturbation optimized on a single video is vulnerable to temporal editing and fails to protect unseen videos. (iii) Temporally inconsistent perturbations are not robust to temporal attacks. To address these challenges, we propose Temporally Consistent Universal Adversarial Perturbations (TC-UAP), the first protection method against both reference- and tuning-based video customization. TC-UAP optimizes an identity-level multi-frame UAP over sliding windows from multiple videos, accounting for local temporal dependencies induced by temporal compression in video VAE and enabling a single perturbation to protect unseen videos of varying lengths. Moreover, we introduce intrinsic temporal modeling and an extrinsic surrogate temporal-attack loss, which make the perturbation temporally consistent and robust to unseen temporal attacks. Empirically, quantitative and qualitative results show that TC-UAP achieves the strongest identity protection compared with existing methods under both reference- and tuning-based video customization, and remains robust under multiple unseen temporal attacks.

Abstract

Unlearnable Examples

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