Video Deepfake Abuse: How Company Choices Predictably Shape Misuse Patterns

• URL: http://arxiv.org/abs/2512.11815v1
• Date: Wed, 26 Nov 2025 18:59:43 GMT
• Title: Video Deepfake Abuse: How Company Choices Predictably Shape Misuse Patterns
• Abstract summary: We analyze how a small number of open-weight AI video generation models have become the dominant tools for videorealistic AIG-NCII video generation.<n>We conclude that developers who openly release the weights of capable video generation models contribute to mitigatable downstream harm.
• Score: 19.641521488247996
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In 2022, AI image generators crossed a key threshold, enabling much more efficient and dynamic production of photorealistic deepfake images than before. This enabled opportunities for creative and positive uses of these models. However, it also enabled unprecedented opportunities for the low-effort creation of AI-generated non-consensual intimate imagery (AIG-NCII), including AI-generated child sexual abuse material (AIG-CSAM). Empirically, these harms were principally enabled by a small number of models that were trained on web data with pornographic content, released with open weights, and insufficiently safeguarded. In this paper, we observe ways in which the same patterns are emerging with video generation models in 2025. Specifically, we analyze how a small number of open-weight AI video generation models have become the dominant tools for videorealistic AIG-NCII video generation. We then analyze the literature on model safeguards and conclude that (1) developers who openly release the weights of capable video generation models without appropriate data curation and/or post-training safeguards foreseeably contribute to mitigatable downstream harm, and (2) model distribution platforms that do not proactively moderate individual misuse or models designed for AIG-NCII foreseeably amplify this harm. While there are no perfect defenses against AIG-NCII and AIG-CSAM from open-weight AI models, we argue that risk management by model developers and distributors, informed by emerging safeguard techniques, will substantially affect the future ease of creating AIG-NCII and AIG-CSAM with generative AI video tools.

Related papers

• Retrieval-Driven Training-Free AI-Generated Video Attribution [24.28021197076691]
  Attributing AI-generated videos to their specific generative sources is critical for forensic investigation and legal regulation.<n>We introduce a training-free AI-generated video attribution paradigm that generalizes to large-scale AI-generated video data.<n>Our method achieves strong performance in both AI-generated video detection and attribution, outperforming existing state-of-the-art methods with a Rank-1 accuracy of 20.5% and a mean Average Precision of 16.6%.
  arXiv  Detail & Related papers  (2026-07-31T02:19:25Z)
• CGCE: Classifier-Guided Concept Erasure in Generative Models [53.7410000675294]
  Concept erasure has been developed to remove undesirable concepts from pre-trained models.<n>Existing methods remain vulnerable to adversarial attacks that can regenerate the erased content.<n>We introduce an efficient plug-and-play framework that provides robust concept erasure for diverse generative models.
  arXiv  Detail & Related papers  (2025-11-08T05:38:18Z)
• Controllable Video Generation: A Survey [72.38313362192784]
  We provide a systematic review of controllable video generation, covering both theoretical foundations and recent advances in the field.<n>We begin by introducing the key concepts and commonly used open-source video generation models.<n>We then focus on control mechanisms in video diffusion models, analyzing how different types of conditions can be incorporated into the denoising process to guide generation.
  arXiv  Detail & Related papers  (2025-07-22T06:05:34Z)
• BusterX: MLLM-Powered AI-Generated Video Forgery Detection and Explanation [77.55074597806035]
  GenBuster-200K is a large-scale, high-quality AI-generated video dataset featuring 200K high-resolution video clips.<n>BusterX is a novel AI-generated video detection and explanation framework leveraging multimodal large language model (MLLM) and reinforcement learning.
  arXiv  Detail & Related papers  (2025-05-19T02:06:43Z)
• RealRAG: Retrieval-augmented Realistic Image Generation via Self-reflective Contrastive Learning [54.07026389388881]
  We present the first real-object-based retrieval-augmented generation framework (RealRAG)<n>RealRAG augments fine-grained and unseen novel object generation by learning and retrieving real-world images to overcome the knowledge gaps of generative models.<n>Our framework integrates fine-grained visual knowledge for the generative models, tackling the distortion problem and improving the realism for fine-grained object generation.
  arXiv  Detail & Related papers  (2025-02-02T16:41:54Z)
• Data-Free Model-Related Attacks: Unleashing the Potential of Generative AI [21.815149263785912]
  We introduce the use of generative AI for facilitating model-related attacks, including model extraction, membership inference, and model inversion.<n>Our study reveals that adversaries can launch a variety of model-related attacks against both image and text models in a data-free and black-box manner.<n>This research serves as an important early warning to the community about the potential risks associated with generative AI-powered attacks on deep learning models.
  arXiv  Detail & Related papers  (2025-01-28T03:12:57Z)
• A Grey-box Attack against Latent Diffusion Model-based Image Editing by Posterior Collapse [7.390756739899558]
  Recent advancements in generative AI, particularly Latent Diffusion Models (LDMs), have revolutionized image synthesis and manipulation.<n>We propose the Posterior Collapse Attack (PCA) based on the observation that VAEs suffer from posterior collapse during training.<n>Our method minimizes dependence on the white-box information of target models to get rid of the implicit reliance on model-specific knowledge.
  arXiv  Detail & Related papers  (2024-08-20T14:43:53Z)
• Direct Unlearning Optimization for Robust and Safe Text-to-Image Models [29.866192834825572]
  Unlearning techniques have been developed to remove the model's ability to generate potentially harmful content.<n>These methods are easily bypassed by adversarial attacks, making them unreliable for ensuring the safety of generated images.<n>We propose Direct Unlearning Optimization (DUO), a novel framework for removing Not Safe For Work (NSFW) content from T2I models.
  arXiv  Detail & Related papers  (2024-07-17T08:19:11Z)
• Generated Distributions Are All You Need for Membership Inference Attacks Against Generative Models [29.135008138824023]
  We propose the first generalized membership inference attack against a variety of generative models. Experiments validate that all the generative models are vulnerable to our attack.
  arXiv  Detail & Related papers  (2023-10-30T10:21:26Z)
• AI-Generated Images as Data Source: The Dawn of Synthetic Era [61.879821573066216]
  generative AI has unlocked the potential to create synthetic images that closely resemble real-world photographs. This paper explores the innovative concept of harnessing these AI-generated images as new data sources. In contrast to real data, AI-generated data exhibit remarkable advantages, including unmatched abundance and scalability.
  arXiv  Detail & Related papers  (2023-10-03T06:55:19Z)
• Coincidental Generation [0.5076419064097732]
  Generative A.I. models have emerged as versatile tools across diverse industries, with applications in privacy-preserving data sharing, computational art, personalization of products and services, and immersive entertainment. Here, we introduce a new privacy concern in the adoption and use of generative A.I. models, where a generative model's output is similar enough to an existing entity, beyond those represented in the dataset used to train the model, to be mistaken for it.
  arXiv  Detail & Related papers  (2023-04-03T16:08:22Z)
• Self-Destructing Models: Increasing the Costs of Harmful Dual Uses of Foundation Models [103.71308117592963]
  We present an algorithm for training self-destructing models leveraging techniques from meta-learning and adversarial learning. In a small-scale experiment, we show MLAC can largely prevent a BERT-style model from being re-purposed to perform gender identification.
  arXiv  Detail & Related papers  (2022-11-27T21:43:45Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.