Immune2V: Image Immunization Against Dual-Stream I2V Models

Image-to-video (I2V) generation has the potential for societal harm because it enables the unauthorized animation of static images to create realistic deepfakes. Recent I2V immunization methods protect reference images by introducing imperceptible adversarial perturbations, but remain fragmented across specific architectures and conditioning mechanisms. In this paper, we systematically analyze how reference images participate in modern I2V generation and identify a common dual-stream conditioning paradigm: a $\text{structural stream}$ that provides a visual anchor, and a $\text{semantic stream}$ that interacts with other semantic conditions (e.g. text prompt) throughout the generation process. Motivated by the need to jointly disrupt these two streams, we propose $\text{Immune2V}$ framework which maximizes the deviation between perturbed and clean structural conditioning signals across distinct conditioning mechanisms, while aligning intermediate generative representations with collapse-inducing trajectories across diverse plausible prompts to counteract semantic guidance. Experiments demonstrate that Immune2V provides stronger and more persistent protection than existing baselines, consistently disrupting I2V generation.