I2VGuard: Safeguarding Images against Misuse in Diffusion-based Image-to-Video Models
Dongnan Gui, Xun Guo, Wengang Zhou, Yan Lu
2025Year
3Top-tier citations
Abstract
Original Image Generated video from original image. Guarded Image Generated video from guarded image. Original Image Generated video from original image. Guarded Image Generated video from guarded image. Figure 1 . Results of our I2VGuard. We present original images, guarded images, and their corresponding SVD-generated videos. All results are generated with the same seed. Our method effectively safeguards images from animation in image-to-video generation.
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Cited by top-tier papers3
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- VSG-Safe: Spotting NSFW Video through Cross-Frame EvidenceYuyang Zhang, Xudong Jiang, Yuxuan Song, Yuxiang Sun et al.USENIX Security 2026
- MEDUSA: Motion Elimination in Diffusion Using Spectral AttackHongwei Yu, Daoqing Zha, Xinlong Ding, Jiawei Li et al.ICML 2026
Builds on28
- Denoising Diffusion Probabilistic ModelsJonathan Ho, Ajay Jain, Pieter AbbeelNeurIPS 2020 · 35,902 citations
- A Simple Framework for Contrastive Learning of Visual RepresentationsTing Chen, Simon Kornblith, Mohammad Norouzi, Geoffrey E. HintonICML 2020 · 24,064 citations
- Diffusion Models Beat GANs on Image SynthesisPrafulla Dhariwal, Alexander Quinn NicholNeurIPS 2021 · 13,211 citations
- High-Resolution Image Synthesis with Latent Diffusion ModelsRobin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser et al.CVPR 2022 · 13,123 citations
- Denoising Diffusion Implicit ModelsJiaming Song, Chenlin Meng, Stefano ErmonICLR 2021 · 11,743 citations
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