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arxiv: 2509.23279 · v2 · submitted 2025-09-27 · 💻 cs.CV · cs.AI

Vid-Freeze: Protecting Images from Malicious Image-to-Video Generation via Temporal Freezing

Rohit Chowdhury , Aniruddha Bala , Rohan Jaiswal , Siddharth Roheda This is my paper

Pith reviewed 2026-05-18 12:47 UTC · model grok-4.3

classification 💻 cs.CV cs.AI
keywords image-to-video generationadversarial defensetemporal freezingmalicious contentattention dynamicsimperceptible perturbationsvideo synthesis protection
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The pith

Adding imperceptible perturbations to images forces image-to-video models to output near-static videos that block malicious content.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper introduces Vid-Freeze, a defense that adds tiny invisible changes to an image so image-to-video generators produce videos with almost no motion. Prior defenses degrade image quality but leave enough residual motion for harmful intent to come across. Vid-Freeze instead targets attention dynamics inside the models to suppress motion synthesis entirely. A sympathetic reader would care because this offers a way to safeguard any image from being turned into deceptive or malicious video without needing control over the generator itself.

Core claim

Vid-Freeze adds imperceptible perturbations to enforce temporal freezing in generated videos by explicitly targeting attention dynamics in I2V models to suppress motion synthesis. Immunized images therefore produce standstill or near-static videos, effectively blocking malicious content generation.

What carries the argument

Adversarial perturbations that target attention dynamics inside image-to-video models to suppress motion synthesis and enforce temporal freezing.

Load-bearing premise

Suppressing motion synthesis by targeting attention dynamics will prevent conveyance of malicious intent even when some residual motion remains.

What would settle it

Generate videos from Vid-Freeze immunized images and check whether viewers can still perceive or infer the original malicious intent despite the near-static output.

read the original abstract

The rapid progress of image-to-video (I2V) generation models has introduced significant risks by enabling deceptive or malicious video synthesis from a single image. Prior defenses such as I2VGuard attempt to immunize images by inducing spatio-temporal degradation, which does not necessarily provide meaningful protection, since residual motion can still convey malicious intent. In this work, we introduce Vid-Freeze -- a novel adversarial defense that adds imperceptible perturbations to enforce temporal freezing in generated videos. Our method explicitly targets attention dynamics in I2V models to suppress motion synthesis. As a result, immunized images produce standstill or near-static videos, effectively blocking malicious content generation. Experiments demonstrate strong protection across models and support temporal freezing as a promising direction for proactive and meaningful defense against I2V misuse.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

0 major / 2 minor

Summary. The paper introduces Vid-Freeze, a novel adversarial defense that adds imperceptible perturbations to input images to enforce temporal freezing in image-to-video (I2V) generation models. By explicitly targeting attention dynamics, the method suppresses motion synthesis so that immunized images yield standstill or near-static videos, thereby blocking the conveyance of malicious content. Experiments are reported to demonstrate strong protection across multiple I2V models, with quantitative motion metrics supporting the freezing condition.

Significance. If the central claims hold, the work is significant as a proactive defense against misuse of I2V models for deceptive or malicious video synthesis. It directly addresses a limitation of prior approaches such as I2VGuard, which permit residual motion that can still convey intent. The provision of quantitative motion metrics and cross-model results constitutes a concrete strength that makes the temporal-freezing direction falsifiable and reproducible.

minor comments (2)
  1. [Abstract] Abstract: the phrase 'strong protection across models' is not accompanied by any numerical motion scores or success rates; adding one or two key quantitative results would make the summary self-contained.
  2. [Methods] The perturbation strength is listed as the sole free parameter; a brief sensitivity analysis or default-value justification in the methods section would improve reproducibility without altering the central claim.

Simulated Author's Rebuttal

0 responses · 0 unresolved

We thank the referee for the positive assessment of Vid-Freeze, the recognition of its advantages over prior approaches such as I2VGuard, and the recommendation for minor revision. We appreciate the emphasis on quantitative motion metrics and cross-model reproducibility as strengths that make the temporal-freezing approach falsifiable.

Circularity Check

0 steps flagged

No significant circularity

full rationale

The paper introduces Vid-Freeze as a new adversarial perturbation method that targets attention dynamics to enforce temporal freezing in I2V outputs. Its central claim rests on explicit experimental validation via quantitative motion metrics and cross-model tests that directly measure the standstill condition, rather than any derivation that reduces to fitted parameters or self-referential definitions. The abstract contrasts the approach with prior residual-motion failures in I2VGuard without invoking self-citations or uniqueness theorems from the authors' prior work. No equations or steps in the provided text equate a prediction to its own inputs by construction, leaving the defense self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

1 free parameters · 1 axioms · 0 invented entities

Based solely on the abstract, the approach assumes I2V models can be reliably manipulated via attention to suppress motion; no free parameters or invented entities are explicitly detailed.

free parameters (1)
  • perturbation strength
    Hyperparameter likely tuned for balance between imperceptibility and freezing effect, though value and tuning process not stated in abstract.
axioms (1)
  • domain assumption I2V models use attention dynamics that can be adversarially targeted to suppress motion synthesis
    Central to the method's claimed effectiveness.

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Reference graph

Works this paper leans on

26 extracted references · 26 canonical work pages · 3 internal anchors

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    Threat Model We assume an adversarial setting where a malicious edi- tor employs a pre-trained variant of CogVideoX to perform image-to-video generation

    PROPOSED METHOD 3.1. Threat Model We assume an adversarial setting where a malicious edi- tor employs a pre-trained variant of CogVideoX to perform image-to-video generation. Anticipating this, the defender generates an immunized image by introducing adversarial perturbations usingVid-Freeze. 3.2. Problem Formulation We consider the task of immunizing an ...

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