Retrieval-Driven Training-Free AI-Generated Video Attribution
AI-generated videos are becoming increasingly realistic and difficult to distinguish from authentic ones, which facilitates malicious misuse and poses growing threats to cybersecurity and social governance. Attributing AI-generated videos to their specific generative sources is therefore of critical importance for forensic investigation and legal regulation. However, most existing visual attribution methods focus on images and particularly rely on the image generation model, thereby lacking the
Record details
Published: 31 July 2026
Source: arXiv
Category: Research
Topics: Regulation · Finance, VC & PE
Retrieved: 3 August 2026
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ethics.ai (31 July 2026), “Retrieval-Driven Training-Free AI-Generated Video Attribution,” evidence record 15685, https://ethics.ai/record/15685 (originally published by arXiv).
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