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New benchmark tackles safety risks in text-to-audio-video AI generation

By PulseAugur Editorial · [1 sources] ·

Researchers have introduced AV-SafetyBench, a new benchmark designed to evaluate the safety of text-to-audio-video (T2AV) generation models. This benchmark addresses the limitations of existing safety evaluations by considering risks that may arise from audio tracks or the joint interpretation of audio and visual elements. AV-SafetyBench includes a taxonomy of 13 categories across four axes and uses 5,200 prompts to assess model outputs from full AV, video-only, and audio-only perspectives. Initial evaluations of five open-source T2AV models revealed significant safety concerns, with Full-AV Unsafe Rates ranging from 25.1% to 49.4%. The analysis highlighted that audio-only and joint audio-visual risks are often missed by video-only evaluations, indicating the critical need for comprehensive safety assessments in T2AV generation. AI

IMPACT This benchmark is crucial for identifying and mitigating safety risks in emerging text-to-audio-video AI models, guiding future development towards more responsible AI.

RANK_REASON The item is a research paper introducing a new benchmark for evaluating AI models. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New benchmark tackles safety risks in text-to-audio-video AI generation

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The item is a research paper introducing a new benchmark for evaluating AI models. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Suah Choi, Tae-Young Lee, Gyeong-Moon Park ·

    AV-SafetyBench: A Safety Benchmark for Text-to-Audio-Video Generation

    arXiv:2609.06991v1 Announce Type: cross Abstract: Recent text-to-audio-video (T2AV) models jointly generate video, speech, sound effects, and ambience from a single text prompt. This capability poses new challenges for safety evaluation, as unsafe content may be conveyed through …

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