SilhouetteTell: Practical Video Identification Leveraging Blurred Recordings of Video Subtitles
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Abstract (arXiv)
Video identification attacks pose a significant privacy threat that can reveal videos that victims watch, which may disclose their hobbies, religious beliefs, political leanings, sexual orientation, and health status. Also, video watching history can be used for user profiling or advertising and may result in cyberbullying, discrimination, or blackmail. Existing extensive video inference techniques usually depend on analyzing network traffic generated by streaming online videos. In this work, we observe that the content of a subtitle determines its silhouette displayed on the screen, and identifying each subtitle silhouette also derives the temporal difference between two consecutive subtitles. We then propose SilhouetteTell, a novel video identification attack that combines the spatial and time domain information into a spatiotemporal feature of subtitle silhouettes. SilhouetteTell explores the spatiotemporal correlation between recorded subtitle silhouettes of a video and its subtitle file. It can infer both online and offline videos. Comprehensive experiments on off-the-shelf smartphones confirm the high efficacy of SilhouetteTell for inferring video titles and clips under various settings, including from a distance of up to 40 meters.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 16 of its 68 references that are in the map. First posted on arXiv 31 Oct 2025.
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References · 16
- 2015Deep Residual Learning for Image Recognitioncited by 52,096
- 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,744
- 2015Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networkscited by 9,639
- 2017Mask R-CNNcited by 7,277
- 2013Rich feature hierarchies for accurate object detection and semantic segmentationcited by 2,162
- 2015Show, Attend and Tell: Neural Image Caption Generation with Visual Attentioncited by 1,423
- 2021ClipCap: CLIP Prefix for Image Captioningcited by 559
- 2015An End-to-End Trainable Neural Network for Image-based Sequence Recognition and Its Application to Scene Text Recognitioncited by 520
- 2014Long-term Recurrent Convolutional Networks for Visual Recognition and Descriptioncited by 484
- 2020TVR: A Large-Scale Dataset for Video-Subtitle Moment Retrievalcited by 215
- 2018A Comprehensive Survey of Deep Learning for Image Captioningcited by 214
- 2017Learning non-maximum suppressioncited by 113
4 more
- 2022Deep Learning Approaches on Image Captioning: A Reviewcited by 43
- 2019Towards End-to-End Text Spotting in Natural Scenescited by 19
- 2016I Know What You Saw Last Minute - Encrypted HTTP Adaptive Video Streaming Title Classificationcited by 3
- 2014Speaker-following Video Subtitlescited by 2
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