SNIFR : Boosting Fine-Grained Child Harmful Content Detection Through Audio-Visual Alignment with Cascaded Cross-Transformer

As video-sharing platforms have grown over the past decade, child viewership has surged, increasing the need for precise detection of harmful content like violence or explicit scenes. Malicious users exploit moderation systems by embedding unsafe content in minimal frames to evade detection. While prior research has focused on visual cues and advanced such fine-grained detection, audio features remain underexplored. In this study, we embed audio cues with visual for fine-grained child harmful content detection and introduce SNIFR, a novel framework for effective alignment. SNIFR employs a transformer encoder for intra-modality interaction, followed by a cascaded cross-transformer for inter-modality alignment. Our approach achieves superior performance over unimodal and baseline fusion methods, setting a new state-of-the-art.

@article{phukan2025_2506.03378,
  title={ SNIFR : Boosting Fine-Grained Child Harmful Content Detection Through Audio-Visual Alignment with Cascaded Cross-Transformer },
  author={ Orchid Chetia Phukan and Mohd Mujtaba Akhtar and Girish and Swarup Ranjan Behera and Abu Osama Siddiqui and Sarthak Jain and Priyabrata Mallick and Jaya Sai Kiran Patibandla and Pailla Balakrishna Reddy and Arun Balaji Buduru and Rajesh Sharma },
  journal={arXiv preprint arXiv:2506.03378},
  year={ 2025 }
}